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HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation | Ce Zhang, Simon Stepputtis, Joseph Campbell, Katia Sycara, Yaqi Xie | Being able to understand visual scenes is a precursor for many downstream tasks including autonomous driving robotics and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however many existing approaches assume undisturbed vision i.e. the absence of real-world corruptions such as fog snow smoke as well as non-uniform perturbations like sun glare or water drops. In this work we propose a novel SGG benchmark containing procedurally generated weather corruptions and other transformations over the Visual Genome dataset. Further we introduce a corresponding approach Hierarchical Knowledge Enhanced Robust Scene Graph Generation (HiKER-SGG) providing a strong baseline for scene graph generation under such challenging setting. At its core HiKER-SGG utilizes a hierarchical knowledge graph in order to refine its predictions from coarse initial estimates to detailed predictions. In our extensive experiments we show that HiKER-SGG does not only demonstrate superior performance on corrupted images in a zero-shot manner but also outperforms current state-of-the-art methods on uncorrupted SGG tasks. Code is available at https://github.com/zhangce01/HiKER-SGG. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_HiKER-SGG_Hierarchical_Knowledge_Enhanced_Robust_Scene_Graph_Generation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HiKER-SGG_Hierarchical_Knowledge_Enhanced_Robust_Scene_Graph_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HiKER-SGG_Hierarchical_Knowledge_Enhanced_Robust_Scene_Graph_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_HiKER-SGG_Hierarchical_Knowledge_CVPR_2024_supplemental.pdf | null |
DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors | Biwen Lei, Kai Yu, Mengyang Feng, Miaomiao Cui, Xuansong Xie | Text-guided domain adaptation and generation of 3D-aware portraits find many applications in various fields. However due to the lack of training data and the challenges in handling the high variety of geometry and appearance the existing methods for these tasks suffer from issues like inflexibility instability and low fidelity. In this paper we propose a novel framework DiffusionGAN3D which boosts text-guided 3D domain adaptation and generation by combining 3D GANs and diffusion priors. Specifically we integrate the pre-trained 3D generative models (e.g. EG3D) and text-to-image diffusion models. The former provides a strong foundation for stable and high-quality avatar generation from text. And the diffusion models in turn offer powerful priors and guide the 3D generator finetuning with informative direction to achieve flexible and efficient text-guided domain adaptation. To enhance the diversity in domain adaptation and the generation capability in text-to-avatar we introduce the relative distance loss and case-specific learnable triplane respectively. Besides we design a progressive texture refinement module to improve the texture quality for both tasks above. Extensive experiments demonstrate that the proposed framework achieves excellent results in both domain adaptation and text-to-avatar tasks outperforming existing methods in terms of generation quality and efficiency. The project homepage is at https://younglbw.github.io/DiffusionGAN3D-homepage/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lei_DiffusionGAN3D_Boosting_Text-guided_3D_Generation_and_Domain_Adaptation_by_Combining_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.16837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lei_DiffusionGAN3D_Boosting_Text-guided_3D_Generation_and_Domain_Adaptation_by_Combining_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lei_DiffusionGAN3D_Boosting_Text-guided_3D_Generation_and_Domain_Adaptation_by_Combining_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lei_DiffusionGAN3D_Boosting_Text-guided_CVPR_2024_supplemental.pdf | null |
Physics-Aware Hand-Object Interaction Denoising | Haowen Luo, Yunze Liu, Li Yi | The credibility and practicality of a reconstructed hand-object interaction sequence depend largely on its physical plausibility. However due to high occlusions during hand-object interaction physical plausibility remains a challenging criterion for purely vision-based tracking methods. To address this issue and enhance the results of existing hand trackers this paper proposes a novel physically-aware hand motion de-noising method. Specifically we introduce two learned loss terms that explicitly capture two crucial aspects of physical plausibility: grasp credibility and manipulation feasibility. These terms are used to train a physically-aware de-noising network. Qualitative and quantitative experiments demonstrate that our approach significantly improves both fine-grained physical plausibility and overall pose accuracy surpassing current state-of-the-art de-noising methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_Physics-Aware_Hand-Object_Interaction_Denoising_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.11481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Physics-Aware_Hand-Object_Interaction_Denoising_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Physics-Aware_Hand-Object_Interaction_Denoising_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Luo_Physics-Aware_Hand-Object_Interaction_CVPR_2024_supplemental.zip | null |
VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction | Jiaqi Lin, Zhihao Li, Xiao Tang, Jianzhuang Liu, Shiyong Liu, Jiayue Liu, Yangdi Lu, Xiaofei Wu, Songcen Xu, Youliang Yan, Wenming Yang | Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes scaling it up to large scenes poses challenges due to limited video memory long optimization time and noticeable appearance variations. To address these challenges we present VastGaussian the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. We propose a progressive partitioning strategy to divide a large scene into multiple cells where the training cameras and point cloud are properly distributed with an airspace-aware visibility criterion. These cells are merged into a complete scene after parallel optimization. We also introduce decoupled appearance modeling into the optimization process to reduce appearance variations in the rendered images. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets enabling fast optimization and high-fidelity real-time rendering. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lin_VastGaussian_Vast_3D_Gaussians_for_Large_Scene_Reconstruction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_VastGaussian_Vast_3D_Gaussians_for_Large_Scene_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_VastGaussian_Vast_3D_Gaussians_for_Large_Scene_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | null | null |
Edit One for All: Interactive Batch Image Editing | Thao Nguyen, Utkarsh Ojha, Yuheng Li, Haotian Liu, Yong Jae Lee | In recent years image editing has advanced remarkably. With increased human control it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change to straight up dragging the contents of the image in an interactive point-based manner. However most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g. make the face frontal) our method can automatically transfer that edit to other test images so that regardless of their initial state (pose) they all arrive at the same final state (e.g. all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods while having more visual consistency and saving significant time and human effort. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nguyen_Edit_One_for_All_Interactive_Batch_Image_Editing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.10219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Edit_One_for_All_Interactive_Batch_Image_Editing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Edit_One_for_All_Interactive_Batch_Image_Editing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nguyen_Edit_One_for_CVPR_2024_supplemental.pdf | null |
Rethinking Boundary Discontinuity Problem for Oriented Object Detection | Hang Xu, Xinyuan Liu, Haonan Xu, Yike Ma, Zunjie Zhu, Chenggang Yan, Feng Dai | Oriented object detection has been developed rapidly in the past few years where rotation equivariance is crucial for detectors to predict rotated boxes. It is expected that the prediction can maintain the corresponding rotation when objects rotate but severe mutation in angular prediction is sometimes observed when objects rotate near the boundary angle which is well-known boundary discontinuity problem. The problem has been long believed to be caused by the sharp loss increase at the angular boundary and widely used joint-optim IoU-like methods deal with this problem by loss-smoothing. However we experimentally find that even state-of-the-art IoU-like methods actually fail to solve the problem. On further analysis we find that the key to solution lies in encoding mode of the smoothing function rather than in joint or independent optimization. In existing IoU-like methods the model essentially attempts to fit the angular relationship between box and object where the break point at angular boundary makes the predictions highly unstable. To deal with this issue we propose a dual-optimization paradigm for angles. We decouple reversibility and joint-optim from single smoothing function into two distinct entities which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters. Extensive experiments on multiple datasets show that boundary discontinuity problem is well-addressed. Moreover typical IoU-like methods are improved to the same level without obvious performance gap. The code is available at https://github.com/hangxu-cv/cvpr24acm. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.10061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Rethinking_Boundary_Discontinuity_CVPR_2024_supplemental.pdf | null |
Deformable One-shot Face Stylization via DINO Semantic Guidance | Yang Zhou, Zichong Chen, Hui Huang | This paper addresses the complex issue of one-shot face stylization focusing on the simultaneous consideration of appearance and structure where previous methods have fallen short. We explore deformation-aware face stylization that diverges from traditional single-image style reference opting for a real-style image pair instead. The cornerstone of our method is the utilization of a self-supervised vision transformer specifically DINO-ViT to establish a robust and consistent facial structure representation across both real and style domains. Our stylization process begins by adapting the StyleGAN generator to be deformation-aware through the integration of spatial transformers (STN). We then introduce two innovative constraints for generator fine-tuning under the guidance of DINO semantics: i) a directional deformation loss that regulates directional vectors in DINO space and ii) a relative structural consistency constraint based on DINO token self-similarities ensuring diverse generation. Additionally style-mixing is employed to align the color generation with the reference minimizing inconsistent correspondences. This framework delivers enhanced deformability for general one-shot face stylization achieving notable efficiency with a fine-tuning duration of approximately 10 minutes. Extensive qualitative and quantitative comparisons demonstrate our superiority over state-of-the-art one-shot face stylization methods. Code is available at https://github.com/zichongc/DoesFS | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Deformable_One-shot_Face_Stylization_via_DINO_Semantic_Guidance_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.00459 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Deformable_One-shot_Face_Stylization_via_DINO_Semantic_Guidance_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Deformable_One-shot_Face_Stylization_via_DINO_Semantic_Guidance_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_Deformable_One-shot_Face_CVPR_2024_supplemental.pdf | null |
SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers | Jonathan F. Carter, João Jorge, Oliver Gibson, Lionel Tarassenko | Advances in camera-based physiological monitoring have enabled the robust non-contact measurement of respiration and the cardiac pulse which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography which is cumbersome expensive to administer and hence unsuitable for longer-term clinical studies. In this paper we introduce SleepVST a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video enabling fully contact-free sleep staging. Using a video dataset of 50 nights we achieve a total accuracy of 78.8% and a Cohen's \kappa of 0.71 in four-class video-based sleep staging setting a new state-of-the-art in the domain. | https://openaccess.thecvf.com/content/CVPR2024/papers/Carter_SleepVST_Sleep_Staging_from_Near-Infrared_Video_Signals_using_Pre-Trained_Transformers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03831 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Carter_SleepVST_Sleep_Staging_from_Near-Infrared_Video_Signals_using_Pre-Trained_Transformers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Carter_SleepVST_Sleep_Staging_from_Near-Infrared_Video_Signals_using_Pre-Trained_Transformers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Carter_SleepVST_Sleep_Staging_CVPR_2024_supplemental.pdf | null |
Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis | Yanzuo Lu, Manlin Zhang, Andy J Ma, Xiaohua Xie, Jianhuang Lai | Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts we develop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages and thus circumventing the potential overfitting problem. To generate more realistic texture details a hybrid-granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://github.com/YanzuoLu/CFLD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lu_Coarse-to-Fine_Latent_Diffusion_for_Pose-Guided_Person_Image_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Coarse-to-Fine_Latent_Diffusion_for_Pose-Guided_Person_Image_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Coarse-to-Fine_Latent_Diffusion_for_Pose-Guided_Person_Image_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_Coarse-to-Fine_Latent_Diffusion_CVPR_2024_supplemental.pdf | null |
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models | Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka | Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue we propose a novel framework that embeds personal watermarks in the generation of adversarial examples. Such examples can force DMs to generate images with visible watermarks and prevent DMs from imitating unauthorized images. We construct a generator based on conditional adversarial networks and design three losses (adversarial loss GAN loss and perturbation loss) to generate adversarial examples that have subtle perturbation but can effectively attack DMs to prevent copyright violations. Training a generator for a personal watermark by our method only requires 5-10 samples within 2-3 minutes and once the generator is trained it can generate adversarial examples with that watermark significantly fast (0.2s per image). We conduct extensive experiments in various conditional image-generation scenarios. Compared to existing methods that generate images with chaotic textures our method adds visible watermarks on the generated images which is a more straightforward way to indicate copyright violations. We also observe that our adversarial examples exhibit good transferability across unknown generative models. Therefore this work provides a simple yet powerful way to protect copyright from DM-based imitation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Watermark-embedded_Adversarial_Examples_for_Copyright_Protection_against_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.09401 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Watermark-embedded_Adversarial_Examples_for_Copyright_Protection_against_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Watermark-embedded_Adversarial_Examples_for_Copyright_Protection_against_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Watermark-embedded_Adversarial_Examples_CVPR_2024_supplemental.pdf | null |
TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model | Hantao Yao, Rui Zhang, Changsheng Xu | Prompt tuning represents a valuable technique for adapting pre-trained visual-language models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods propose a set of learnable domain-shared or image-conditional textual tokens to facilitate the generation of task-specific textual classifiers. However those textual tokens have a limited generalization ability regarding unseen domains as they cannot dynamically adjust to the distribution of testing classes. To tackle this issue we present a novel Textual-based Class-aware Prompt tuning(TCP) that explicitly incorporates prior knowledge about classes to enhance their discriminability. The critical concept of TCP involves leveraging Textual Knowledge Embedding (TKE) to map the high generalizability of class-level textual knowledge into class aware textual tokens. By seamlessly integrating these class-aware prompts into the Text Encoder a dynamic class-aware classifier is generated to enhance discriminability for unseen domains. During inference TKE dynamically generates class-aware prompts related to the unseen classes. Comprehensive evaluations demonstrate that TKE serves as a plug-and-play module effortlessly combinable with existing methods. Furthermore TCP consistently achieves superior performance while demanding less training time. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yao_TCPTextual-based_Class-aware_Prompt_tuning_for_Visual-Language_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18231 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_TCPTextual-based_Class-aware_Prompt_tuning_for_Visual-Language_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_TCPTextual-based_Class-aware_Prompt_tuning_for_Visual-Language_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yao_TCPTextual-based_Class-aware_Prompt_CVPR_2024_supplemental.pdf | null |
OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers | Han Liang, Jiacheng Bao, Ruichi Zhang, Sihan Ren, Yuecheng Xu, Sibei Yang, Xin Chen, Jingyi Yu, Lan Xu | We have recently seen tremendous progress in realistic text-to-motion generation. Yet the existing methods often fail or produce implausible motions with unseen text inputs which limits the applications. In this paper we present OMG a novel framework which enables compelling motion generation from zero-shot open-vocabulary text prompts. Our key idea is to carefully tailor the pretrain-then-finetune paradigm into the text-to-motion generation. At the pre-training stage our model improves the generation ability by learning the rich out-of-domain inherent motion traits. To this end we scale up a large unconditional diffusion model up to 1B parameters so as to utilize the massive unlabeled motion data up to over 20M motion instances. At the subsequent fine-tuning stage we introduce motion ControlNet which incorporates text prompts as conditioning information through a trainable copy of the pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block. MoC block adaptively recognizes various ranges of the sub-motions with a cross-attention mechanism and processes them separately with the text-token-specific experts. Such a design effectively aligns the CLIP token embeddings of text prompts to various ranges of compact and expressive motion features. Extensive experiments demonstrate that our OMG achieves significant improvements over the state-of-the-art methods on zero-shot text-to-motion generation. Project page: https://tr3e.github.io/omg-page. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_OMG_Towards_Open-vocabulary_Motion_Generation_via_Mixture_of_Controllers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.08985 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_OMG_Towards_Open-vocabulary_Motion_Generation_via_Mixture_of_Controllers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_OMG_Towards_Open-vocabulary_Motion_Generation_via_Mixture_of_Controllers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liang_OMG_Towards_Open-vocabulary_CVPR_2024_supplemental.pdf | null |
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding | Shuhuai Ren, Linli Yao, Shicheng Li, Xu Sun, Lu Hou | This work proposes TimeChat a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally we construct an instruction-tuning dataset encompassing 6 tasks and a total of 125K instances to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks such as dense captioning temporal grounding and highlight detection demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2 +5.8 HIT@1 on QVHighlights and +27.5 R@1 (IoU=0.5) on Charades-STA compared to state-of-the-art video large language models holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ren_TimeChat_A_Time-sensitive_Multimodal_Large_Language_Model_for_Long_Video_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_TimeChat_A_Time-sensitive_Multimodal_Large_Language_Model_for_Long_Video_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_TimeChat_A_Time-sensitive_Multimodal_Large_Language_Model_for_Long_Video_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ren_TimeChat_A_Time-sensitive_CVPR_2024_supplemental.pdf | null |
Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models | Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, Karsten Kreis | Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here we instead focus on the underexplored text-to-4D setting and synthesize dynamic animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work we pursue a novel compositional generation-based approach and combine text-to-image text-to-video and 3D-aware multiview diffusion models to provide feedback during 4D object optimization thereby simultaneously enforcing temporal consistency high-quality visual appearance and realistic geometry. Our method called Align Your Gaussians (AYG) leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation different 4D animations can be seamlessly combined as we demonstrate. AYG opens up promising avenues for animation simulation and digital content creation as well as synthetic data generation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ling_Align_Your_Gaussians_Text-to-4D_with_Dynamic_3D_Gaussians_and_Composed_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.13763 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ling_Align_Your_Gaussians_Text-to-4D_with_Dynamic_3D_Gaussians_and_Composed_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ling_Align_Your_Gaussians_Text-to-4D_with_Dynamic_3D_Gaussians_and_Composed_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ling_Align_Your_Gaussians_CVPR_2024_supplemental.pdf | null |
PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation | Jinfeng Xu, Siyuan Yang, Xianzhi Li, Yuan Tang, Yixue Hao, Long Hu, Min Chen | Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge due to a closed-set and static perspective of the real world which would induce the intelligent agent to make bad decisions. To address this problem we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that includes (i) a lightweight U-decoder branch to identify unknown classes by estimating the uncertainties (ii) a flexible pseudo-labeling scheme to supply geometry features along with probability distribution features of unknown classes by generating pseudo labels and (iii) an incremental knowledge distillation strategy to incorporate novel classes into the existing knowledge base gradually. Our framework enables the model to behave like human beings which could recognize unknown objects and incrementally learn them with the corresponding knowledge. Experimental results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF outperforms other methods by a large margin in both important tasks of open world semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_PDF_A_Probability-Driven_Framework_for_Open_World_3D_Point_Cloud_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00979 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_PDF_A_Probability-Driven_Framework_for_Open_World_3D_Point_Cloud_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_PDF_A_Probability-Driven_Framework_for_Open_World_3D_Point_Cloud_CVPR_2024_paper.html | CVPR 2024 | null | null |
Test-Time Domain Generalization for Face Anti-Spoofing | Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Shouhong Ding, Lizhuang Ma | Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems against presentation attacks. While domain generalization (DG) methods have been developed to enhance FAS performance they predominantly focus on learning domain-invariant features during training which may not guarantee generalizability to unseen data that differs largely from the source distributions. Our insight is that testing data can serve as a valuable resource to enhance the generalizability beyond mere evaluation for DG FAS. In this paper we introduce a novel Test-Time Domain Generalization (TTDG) framework for FAS which leverages the testing data to boost the model's generalizability. Our method consisting of Test-Time Style Projection (TTSP) and Diverse Style Shifts Simulation (DSSS) effectively projects the unseen data to the seen domain space. In particular we first introduce the innovative TTSP to project the styles of the arbitrarily unseen samples of the testing distribution to the known source space of the training distributions. We then design the efficient DSSS to synthesize diverse style shifts via learnable style bases with two specifically designed losses in a hyperspherical feature space. Our method eliminates the need for model updates at the test time and can be seamlessly integrated into not only the CNN but also ViT backbones. Comprehensive experiments on widely used cross-domain FAS benchmarks demonstrate our method's state-of-the-art performance and effectiveness. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Test-Time_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Test-Time_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Test-Time_Domain_Generalization_for_Face_Anti-Spoofing_CVPR_2024_paper.html | CVPR 2024 | null | null |
DiffusionMTL: Learning Multi-Task Denoising Diffusion Model from Partially Annotated Data | Hanrong Ye, Dan Xu | Recently there has been an increased interest in the practical problem of learning multiple dense scene understanding tasks from partially annotated data where each training sample is only labeled for a subset of the tasks. The missing of task labels in training leads to low-quality and noisy predictions as can be observed from state-of-the-art methods. To tackle this issue we reformulate the partially-labeled multi-task dense prediction as a pixel-level denoising problem and propose a novel multi-task denoising diffusion framework coined as DiffusionMTL. It designs a joint diffusion and denoising paradigm to model a potential noisy distribution in the task prediction or feature maps and generate rectified outputs for different tasks. To exploit multi-task consistency in denoising we further introduce a Multi-Task Conditioning strategy which can implicitly utilize the complementary nature of the tasks to help learn the unlabeled tasks leading to an improvement in the denoising performance of the different tasks. Extensive quantitative and qualitative experiments demonstrate that the proposed multi-task denoising diffusion model can significantly improve multi-task prediction maps and outperform the state-of-the-art methods on three challenging multi-task benchmarks under two different partial-labeling evaluation settings. The code is available at https://prismformore.github.io/diffusionmtl/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ye_DiffusionMTL_Learning_Multi-Task_Denoising_Diffusion_Model_from_Partially_Annotated_Data_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.15389 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_DiffusionMTL_Learning_Multi-Task_Denoising_Diffusion_Model_from_Partially_Annotated_Data_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_DiffusionMTL_Learning_Multi-Task_Denoising_Diffusion_Model_from_Partially_Annotated_Data_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ye_DiffusionMTL_Learning_Multi-Task_CVPR_2024_supplemental.pdf | null |
Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment | Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, Tiejun Huang | The traditional frame-based cameras that rely on exposure windows for imaging experience motion blur in high-speed scenarios. Frame-based deblurring methods lack reliable motion cues to restore sharp images under extreme blur conditions. The spike camera is a novel neuromorphic visual sensor that outputs spike streams with ultra-high temporal resolution. It can supplement the temporal information lost in traditional cameras and guide motion deblurring. However in real-world scenarios aligning discrete RGB images and continuous spike streams along both temporal and spatial axes is challenging due to the complexity of calibrating their coordinates device displacements in vibrations and time deviations. Misalignment of pixels leads to severe degradation of deblurring. We introduce the first framework for spike-guided motion deblurring without knowing the spatiotemporal alignment between spikes and images. To address the problem we first propose a novel three-stage network containing a basic deblurring net a carefully designed bi-directional deformable aligning module and a flow-based multi-scale fusion net. Experimental results demonstrate that our approach can effectively guide the image deblurring with unknown alignment surpassing the performance of other methods. Public project page: https://github.com/Leozhangjiyuan/UaSDN. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Spike-guided_Motion_Deblurring_with_Unknown_Modal_Spatiotemporal_Alignment_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Spike-guided_Motion_Deblurring_with_Unknown_Modal_Spatiotemporal_Alignment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Spike-guided_Motion_Deblurring_with_Unknown_Modal_Spatiotemporal_Alignment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Spike-guided_Motion_Deblurring_CVPR_2024_supplemental.pdf | null |
VRP-SAM: SAM with Visual Reference Prompt | Yanpeng Sun, Jiahui Chen, Shan Zhang, Xinyu Zhang, Qiang Chen, Gang Zhang, Errui Ding, Jingdong Wang, Zechao Li | In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation creating the VRP-SAM model. In essence VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images including point box scribble and mask. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore VRP-SAM demonstrates strong generalization capabilities allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_VRP-SAM_SAM_with_Visual_Reference_Prompt_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_VRP-SAM_SAM_with_Visual_Reference_Prompt_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_VRP-SAM_SAM_with_Visual_Reference_Prompt_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_VRP-SAM_SAM_with_CVPR_2024_supplemental.pdf | null |
Discriminability-Driven Channel Selection for Out-of-Distribution Detection | Yue Yuan, Rundong He, Yicong Dong, Zhongyi Han, Yilong Yin | Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world environments. Activation-based methods are a key approach in OOD detection working to mitigate overconfident predictions of OOD data. These techniques rectifying anomalous activations enhancing the distinguishability between in-distribution (ID) data and OOD data. However they assume by default that every channel is necessary for OOD detection and rectify anomalous activations in each channel. Empirical evidence has shown that there is a significant difference among various channels in OOD detection and discarding some channels can greatly enhance the performance of OOD detection. Based on this insight we propose \underline D iscriminability-\underline D riven \underline C hannel \underline S election (DDCS) which leverages an adaptive channel selection by estimating the discriminative score of each channel to boost OOD detection. The discriminative score takes inter-class similarity and inter-class variance of training data into account. However the estimation of discriminative score itself is susceptible to anomalous activations. To better estimate score we pre-rectify anomalous activations for each channel mildly. The experimental results show that DDCS achieves state-of-the-art performance on CIFAR and ImageNet-1K benchmarks. Moreover DDCS can generalize to different backbones and OOD scores. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yuan_Discriminability-Driven_Channel_Selection_for_Out-of-Distribution_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Discriminability-Driven_Channel_Selection_for_Out-of-Distribution_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Discriminability-Driven_Channel_Selection_for_Out-of-Distribution_Detection_CVPR_2024_paper.html | CVPR 2024 | null | null |
ManiFPT: Defining and Analyzing Fingerprints of Generative Models | Hae Jin Song, Mahyar Khayatkhoei, Wael AbdAlmageed | Recent works have shown that generative models leave traces of their underlying generative process on the generated samples broadly referred to as fingerprints of a generative model and have studied their utility in detecting synthetic images from real ones. However the extend to which these fingerprints can distinguish between various types of synthetic image and help identify the underlying generative process remain under-explored. In particular the very definition of a fingerprint remains unclear to our knowledge. To that end in this work we formalize the definition of artifact and fingerprint in generative models propose an algorithm for computing them in practice and finally study its effectiveness in distinguishing a large array of different generative models. We find that using our proposed definition can significantly improve the performance on the task of identifying the underlying generative process from samples (model attribution) compared to existing methods. Additionally we study the structure of the fingerprints and observe that it is very predictive of the effect of different design choices on the generative process. | https://openaccess.thecvf.com/content/CVPR2024/papers/Song_ManiFPT_Defining_and_Analyzing_Fingerprints_of_Generative_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.10401 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Song_ManiFPT_Defining_and_Analyzing_Fingerprints_of_Generative_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Song_ManiFPT_Defining_and_Analyzing_Fingerprints_of_Generative_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Song_ManiFPT_Defining_and_CVPR_2024_supplemental.pdf | null |
Real-time 3D-aware Portrait Video Relighting | Ziqi Cai, Kaiwen Jiang, Shu-Yu Chen, Yu-Kun Lai, Hongbo Fu, Boxin Shi, Lin Gao | Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF). Given an input portrait video our method can synthesize talking faces under both novel views and novel lighting conditions with a photo-realistic and disentangled 3D representation. Specifically we infer an albedo tri-plane as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. We also leverage a temporal consistency network to ensure smooth transitions and reduce flickering artifacts. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality lighting error lighting instability temporal consistency and inference speed. We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_Real-time_3D-aware_Portrait_Video_Relighting_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Real-time_3D-aware_Portrait_Video_Relighting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Real-time_3D-aware_Portrait_Video_Relighting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cai_Real-time_3D-aware_Portrait_CVPR_2024_supplemental.pdf | null |
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting | Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, Siyu Tang | We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) achieve high-quality novel-view/novel-pose image synthesis but often require days of training and are extremely slow at inference time. Recently the community has explored fast grid structures for efficient training of clothed avatars. Albeit being extremely fast at training these methods can barely achieve an interactive rendering frame rate with around 15 FPS. In this paper we use 3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct animatable clothed human avatars that can be trained within 30 minutes and rendered at real-time frame rates (50+ FPS). Given the explicit nature of our representation we further introduce as-isometric-as-possible regularizations on both the Gaussian mean vectors and the covariance matrices enhancing the generalization of our model on highly articulated unseen poses. Experimental results show that our method achieves comparable and even better performance compared to state-of-the-art approaches on animatable avatar creation from a monocular input while being 400x and 250x faster in training and inference respectively. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qian_3DGS-Avatar_Animatable_Avatars_via_Deformable_3D_Gaussian_Splatting_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qian_3DGS-Avatar_Animatable_Avatars_via_Deformable_3D_Gaussian_Splatting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qian_3DGS-Avatar_Animatable_Avatars_via_Deformable_3D_Gaussian_Splatting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qian_3DGS-Avatar_Animatable_Avatars_CVPR_2024_supplemental.pdf | null |
Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos | Mehmet Saygin Seyfioglu, Wisdom O. Ikezogwo, Fatemeh Ghezloo, Ranjay Krishna, Linda Shapiro | Diagnosis in histopathology requires a global whole slide images (WSIs) analysis requiring pathologists to compound evidence from different WSI patches. The gigapixel scale of WSIs poses a challenge for histopathology multi-modal models. Training multi-model models for histopathology requires instruction tuning datasets which currently contain information for individual image patches without a spatial grounding of the concepts within each patch and without a wider view of the WSI. To bridge this gap we introduce QUILT-INSTRUCT a large-scale dataset of 107131 histopathology-specific instruction question/answer pairs grounded within diagnostically relevant image patches that make up the WSI. Our dataset is collected by leveraging educational histopathology videos from YouTube which provides spatial localization of narrations by automatically extracting the narrators' cursor positions. QUILT-INSTRUCT supports contextual reasoning by extracting diagnosis and supporting facts from the entire WSI. Using QUILT-INSTRUCT we train QUILT-LLAVA which can reason beyond the given single image patch enabling diagnostic reasoning across patches. To evaluate QUILT-LLAVA we propose a comprehensive evaluation dataset created from 985 images and 1283 human-generated question-answers. We also thoroughly evaluate QUILT-LLAVA using public histopathology datasets where QUILT-LLAVA significantly outperforms SOTA by over 10% on relative GPT-4 score and 4% and 9% on open and closed set VQA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Seyfioglu_Quilt-LLaVA_Visual_Instruction_Tuning_by_Extracting_Localized_Narratives_from_Open-Source_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Seyfioglu_Quilt-LLaVA_Visual_Instruction_Tuning_by_Extracting_Localized_Narratives_from_Open-Source_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Seyfioglu_Quilt-LLaVA_Visual_Instruction_Tuning_by_Extracting_Localized_Narratives_from_Open-Source_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Seyfioglu_Quilt-LLaVA_Visual_Instruction_CVPR_2024_supplemental.pdf | null |
Traffic Scene Parsing through the TSP6K Dataset | Peng-Tao Jiang, Yuqi Yang, Yang Cao, Qibin Hou, Ming-Ming Cheng, Chunhua Shen | Traffic scene perception in computer vision is a critically important task to achieve intelligent cities. To date most existing datasets focus on autonomous driving scenes. We observe that the models trained on those driving datasets often yield unsatisfactory results on traffic monitoring scenes. However little effort has been put into improving the traffic monitoring scene understanding mainly due to the lack of specific datasets. To fill this gap we introduce a specialized traffic monitoring dataset termed TSP6K containing images from the traffic monitoring scenario with high-quality pixel-level and instance-level annotations. The TSP6K dataset captures more crowded traffic scenes with several times more traffic participants than the existing driving scenes. We perform a detailed analysis of the dataset and comprehensively evaluate previous popular scene parsing methods instance segmentation methods and unsupervised domain adaption methods. Furthermore considering the vast difference in instance sizes we propose a detail refining decoder for scene parsing which recovers the details of different semantic regions in traffic scenes owing to the proposed TSP6K dataset. Experiments show its effectiveness in parsing the traffic monitoring scenes. Code and dataset are available at https://github.com/PengtaoJiang/TSP6K. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_Traffic_Scene_Parsing_through_the_TSP6K_Dataset_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.02835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_Traffic_Scene_Parsing_through_the_TSP6K_Dataset_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_Traffic_Scene_Parsing_through_the_TSP6K_Dataset_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jiang_Traffic_Scene_Parsing_CVPR_2024_supplemental.pdf | null |
Style Aligned Image Generation via Shared Attention | Amir Hertz, Andrey Voynov, Shlomi Fruchter, Daniel Cohen-Or | Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields generating visually compelling outputs from textual prompts. However controlling these models to ensure consistent style remains challenging with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper we introduce StyleAligned a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity underscoring its efficacy in achieving consistent style across various inputs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hertz_Style_Aligned_Image_Generation_via_Shared_Attention_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hertz_Style_Aligned_Image_Generation_via_Shared_Attention_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hertz_Style_Aligned_Image_Generation_via_Shared_Attention_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hertz_Style_Aligned_Image_CVPR_2024_supplemental.pdf | null |
E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator | Wenjun Wu, Lingling Zhang, Jun Liu, Xi Tang, Yaxian Wang, Shaowei Wang, Qianying Wang | Geometry Problem Solving has drawn growing attention recently due to its application prospects in intelligent education field. However existing methods are still inadequate to meet the needs of practical application suffering from the following limitations: 1) explainability is not ensured which is essential in real teaching scenarios; 2) the small scale and incomplete annotation of existing datasets make it hard for model to comprehend geometric knowledge. To tackle the above problems we propose a novel method called Explainable Geometry Problem Solving (E-GPS). E-GPS first parses the geometric diagram and problem text into unified formal language representations. Then the answer and explainable reasoning and solving steps are obtained by a Top-Down Problem Solver (TD-PS) which innovatively solves the problem from the target and focuses on what is needed. To alleviate the data issues a Bottom-Up Problem Generator (BU-PG) is devised to augment the data set with various well-annotated constructed geometry problems. It enables us to train an enhanced theorem predictor with a better grasp of theorem knowledge which further improves the efficiency of TD-PS. Extensive experiments demonstrate that E-GPS maintains comparable solving performances with fewer steps and provides outstanding explainability. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_E-GPS_Explainable_Geometry_Problem_Solving_via_Top-Down_Solver_and_Bottom-Up_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_E-GPS_Explainable_Geometry_Problem_Solving_via_Top-Down_Solver_and_Bottom-Up_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_E-GPS_Explainable_Geometry_Problem_Solving_via_Top-Down_Solver_and_Bottom-Up_CVPR_2024_paper.html | CVPR 2024 | null | null |
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features | Thomas Wimmer, Peter Wonka, Maks Ovsjanikov | With the immense growth of dataset sizes and computing resources in recent years so-called foundation models have become popular in NLP and vision tasks. In this work we propose to explore foundation models for the task of keypoint detection on 3D shapes. A unique characteristic of keypoint detection is that it requires semantic and geometric awareness while demanding high localization accuracy. To address this problem we propose first to back-project features from large pre-trained 2D vision models onto 3D shapes and employ them for this task. We show that we obtain robust 3D features that contain rich semantic information and analyze multiple candidate features stemming from different 2D foundation models. Second we employ a keypoint candidate optimization module which aims to match the average observed distribution of keypoints on the shape and is guided by the back-projected features. The resulting approach achieves a new state of the art for few-shot keypoint detection on the KeyPointNet dataset almost doubling the performance of the previous best methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wimmer_Back_to_3D_Few-Shot_3D_Keypoint_Detection_with_Back-Projected_2D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wimmer_Back_to_3D_Few-Shot_3D_Keypoint_Detection_with_Back-Projected_2D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wimmer_Back_to_3D_Few-Shot_3D_Keypoint_Detection_with_Back-Projected_2D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wimmer_Back_to_3D_CVPR_2024_supplemental.pdf | null |
Fourier Priors-Guided Diffusion for Zero-Shot Joint Low-Light Enhancement and Deblurring | Xiaoqian Lv, Shengping Zhang, Chenyang Wang, Yichen Zheng, Bineng Zhong, Chongyi Li, Liqiang Nie | Existing joint low-light enhancement and deblurring methods learn pixel-wise mappings from paired synthetic data which results in limited generalization in real-world scenes. While some studies explore the rich generative prior of pre-trained diffusion models they typically rely on the assumed degradation process and cannot handle unknown real-world degradations well. To address these problems we propose a novel zero-shot framework FourierDiff which embeds Fourier priors into a pre-trained diffusion model to harmoniously handle the joint degradation of luminance and structures. FourierDiff is appealing in its relaxed requirements on paired training data and degradation assumptions. The key zero-shot insight is motivated by image characteristics in the Fourier domain: most luminance information concentrates on amplitudes while structure and content information are closely related to phases. Based on this observation we decompose the sampled results of the reverse diffusion process in the Fourier domain and take advantage of the amplitude of the generative prior to align the enhanced brightness with the distribution of natural images. To yield a sharp and content-consistent enhanced result we further design a spatial-frequency alternating optimization strategy to progressively refine the phase of the input. Extensive experiments demonstrate the superior effectiveness of the proposed method especially in real-world scenes. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lv_Fourier_Priors-Guided_Diffusion_for_Zero-Shot_Joint_Low-Light_Enhancement_and_Deblurring_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lv_Fourier_Priors-Guided_Diffusion_for_Zero-Shot_Joint_Low-Light_Enhancement_and_Deblurring_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lv_Fourier_Priors-Guided_Diffusion_for_Zero-Shot_Joint_Low-Light_Enhancement_and_Deblurring_CVPR_2024_paper.html | CVPR 2024 | null | null |
Neural Markov Random Field for Stereo Matching | Tongfan Guan, Chen Wang, Yun-Hui Liu | Stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accuracy compared to end-to-end deep models. While deep learning representations have greatly improved the unary terms of the MRF models the overall accuracy is still severely limited by the hand-crafted pairwise terms and message passing. To address these issues we propose a neural MRF model where both potential functions and message passing are designed using data-driven neural networks. Our fully data-driven model is built on the foundation of variational inference theory to prevent convergence issues and retain stereo MRF's graph inductive bias. To make the inference tractable and scale well to high-resolution images we also propose a Disparity Proposal Network (DPN) to adaptively prune the search space of disparity. The proposed approach ranks 1^ st on both KITTI 2012 and 2015 leaderboards among all published methods while running faster than 100 ms. This approach significantly outperforms prior global methods e.g. lowering D1 metric by more than 50% on KITTI 2015. In addition our method exhibits strong cross-domain generalization and can recover sharp edges. The codes at https://github.com/aeolusguan/NMRF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Guan_Neural_Markov_Random_Field_for_Stereo_Matching_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11193 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_Neural_Markov_Random_Field_for_Stereo_Matching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_Neural_Markov_Random_Field_for_Stereo_Matching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guan_Neural_Markov_Random_CVPR_2024_supplemental.pdf | null |
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving | Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang | In autonomous driving predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency on the road. To this end we propose Drive-WM the first driving world model compatible with existing end-to-end planning models. Through a joint spatial-temporal modeling facilitated by view factorization our model is the first to generate high-fidelity multiview videos. Building on its powerful generation ability we showcase the potential of applying the world model for safe driving planning for the first time. Our Drive-WM enables driving into multiple futures based on distinct driving maneuvers and determines the optimal trajectory according to the image-based rewards. Evaluation on real-world driving datasets verifies that our method could generate high-quality consistent and controllable multiview videos opening up possibilities for real-world simulations and safe planning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Driving_into_the_Future_Multiview_Visual_Forecasting_and_Planning_with_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17918 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Driving_into_the_Future_Multiview_Visual_Forecasting_and_Planning_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Driving_into_the_Future_Multiview_Visual_Forecasting_and_Planning_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Driving_into_the_CVPR_2024_supplemental.pdf | null |
OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies | Lingdong Kong, Youquan Liu, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi | Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event camera sensing. The difficulties in interpreting and annotating event data limit its scalability. While domain adaptation from images to event data can help to mitigate this issue there exist data representational differences that require additional effort to resolve. In this work for the first time we synergize information from image text and event-data domains and introduce OpenESS to enable scalable ESS in an open-world annotation-efficient manner. We achieve this goal by transferring the semantically rich CLIP knowledge from image-text pairs to event streams. To pursue better cross-modality adaptation we propose a frame-to-event contrastive distillation and a text-to-event semantic consistency regularization. Experimental results on popular ESS benchmarks showed our approach outperforms existing methods. Notably we achieve 53.93% and 43.31% mIoU on DDD17 and DSEC-Semantic without using either event or frame labels. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kong_OpenESS_Event-based_Semantic_Scene_Understanding_with_Open_Vocabularies_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.05259 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kong_OpenESS_Event-based_Semantic_Scene_Understanding_with_Open_Vocabularies_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kong_OpenESS_Event-based_Semantic_Scene_Understanding_with_Open_Vocabularies_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kong_OpenESS_Event-based_Semantic_CVPR_2024_supplemental.pdf | null |
Do Vision and Language Encoders Represent the World Similarly? | Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Mohamed El Amine Seddik, Sanath Narayan, Karttikeya Mangalam, Noel E. O'Connor | Aligned text-image encoders such as CLIP have become the de-facto model for vision-language tasks. Furthermore modality-specific encoders achieve impressive performances in their respective domains. This raises a central question: does an alignment exist between uni-modal vision and language encoders since they fundamentally represent the same physical world? Analyzing the latent spaces structure of vision and language models on image-caption benchmarks using the Centered Kernel Alignment (CKA) we find that the representation spaces of unaligned and aligned encoders are semantically similar. In the absence of statistical similarity in aligned encoders like CLIP we show that a possible matching of unaligned encoders exists without any training. We frame this as a seeded graph-matching problem exploiting the semantic similarity between graphs and propose two methods - a Fast Quadratic Assignment Problem optimization and a novel localized CKA metric-based matching/retrieval. We demonstrate the effectiveness of this on several downstream tasks including cross-lingual cross-domain caption matching and image classification. Code available at github.com/mayug/0-shot-llm-vision. | https://openaccess.thecvf.com/content/CVPR2024/papers/Maniparambil_Do_Vision_and_Language_Encoders_Represent_the_World_Similarly_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.05224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Maniparambil_Do_Vision_and_Language_Encoders_Represent_the_World_Similarly_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Maniparambil_Do_Vision_and_Language_Encoders_Represent_the_World_Similarly_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Maniparambil_Do_Vision_and_CVPR_2024_supplemental.pdf | null |
MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction | Xiaolu Liu, Song Wang, Wentong Li, Ruizi Yang, Junbo Chen, Jianke Zhu | Currently high-definition (HD) map construction leans towards a lightweight online generation tendency which aims to preserve timely and reliable road scene information. However map elements contain strong shape priors. Subtle and sparse annotations make current detection-based frameworks ambiguous in locating relevant feature scopes and cause the loss of detailed structures in prediction. To alleviate these problems we propose MGMap a mask-guided approach that effectively highlights the informative regions and achieves precise map element localization by introducing the learned masks. Specifically MGMap employs learned masks based on the enhanced multi-scale BEV features from two perspectives. At the instance level we propose the Mask-activated instance (MAI) decoder which incorporates global instance and structural information into instance queries by the activation of instance masks. At the point level a novel position-guided mask patch refinement (PG-MPR) module is designed to refine point locations from a finer-grained perspective enabling the extraction of point-specific patch information. Compared to the baselines our proposed MGMap achieves a notable improvement of around 10 mAP for different input modalities. Extensive experiments also demonstrate that our approach showcases strong robustness and generalization capabilities. Our code can be found at https://github.com/xiaolul2/MGMap. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_MGMap_Mask-Guided_Learning_for_Online_Vectorized_HD_Map_Construction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_MGMap_Mask-Guided_Learning_for_Online_Vectorized_HD_Map_Construction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_MGMap_Mask-Guided_Learning_for_Online_Vectorized_HD_Map_Construction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_MGMap_Mask-Guided_Learning_CVPR_2024_supplemental.pdf | null |
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild | Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong | We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution high-quality images for model training each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts broadening its application scope and potential. Moreover we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Scaling_Up_to_Excellence_Practicing_Model_Scaling_for_Photo-Realistic_Image_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.13627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Scaling_Up_to_Excellence_Practicing_Model_Scaling_for_Photo-Realistic_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Scaling_Up_to_Excellence_Practicing_Model_Scaling_for_Photo-Realistic_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Scaling_Up_to_CVPR_2024_supplemental.pdf | null |
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models | Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Kaixin Xu, Chunyi Li, Jingwen Hou, Guangtao Zhai, Geng Xue, Wenxiu Sun, Qiong Yan, Weisi Lin | Multi-modality large language models (MLLMs) as represented by GPT-4V have introduced a paradigm shift for visual perception and understanding tasks that a variety of abilities can be achieved within one foundation model. While current MLLMs demonstrate primary low-level visual abilities from the identification of low-level visual attributes (e.g. clarity brightness) to the evaluation on image quality there's still an imperative to further improve the accuracy of MLLMs to substantially alleviate human burdens. To address this we collect the first dataset consisting of human natural language feedback on low-level vision. Each feedback offers a comprehensive description of an image's low-level visual attributes culminating in an overall quality assessment. The constructed Q-Pathway dataset includes 58K detailed human feedbacks on 18973 multi-sourced images with diverse low-level appearance. To ensure MLLMs can adeptly handle diverse queries we further propose a GPT-participated transformation to convert these feedbacks into a rich set of 200K instruction-response pairs termed Q-Instruct. Experimental results indicate that the Q-Instruct consistently elevates various low-level visual capabilities across multiple base models. We anticipate that our datasets can pave the way for a future that foundation models can assist humans on low-level visual tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Q-Instruct_Improving_Low-level_Visual_Abilities_for_Multi-modality_Foundation_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Q-Instruct_Improving_Low-level_Visual_Abilities_for_Multi-modality_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Q-Instruct_Improving_Low-level_Visual_Abilities_for_Multi-modality_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Q-Instruct_Improving_Low-level_CVPR_2024_supplemental.pdf | null |
PoseIRM: Enhance 3D Human Pose Estimation on Unseen Camera Settings via Invariant Risk Minimization | Yanlu Cai, Weizhong Zhang, Yuan Wu, Cheng Jin | Camera-parameter-free multi-view pose estimation is an emerging technique for 3D human pose estimation (HPE). They can infer the camera settings implicitly or explicitly to mitigate the depth uncertainty impact showcasing significant potential in real applications. However due to the limited camera setting diversity in the available datasets the inferred camera parameters are always simply hardcoded into the model during training and not adaptable to the input in inference making the learned models cannot generalize well under unseen camera settings. A natural solution is to artificially synthesize some samples i.e. 2D-3D pose pairs under massive new camera settings. Unfortunately to prevent over-fitting the existing camera setting the number of synthesized samples for each new camera setting should be comparable with that for the existing one which multiplies the scale of training and even makes it computationally prohibitive. In this paper we propose a novel HPE approach under the invariant risk minimization (IRM) paradigm. Precisely we first synthesize 2D poses from myriad camera settings. We then train our model under the IRM paradigm which targets at learning a common optimal model across all camera settings and thus enforces the model to automatically learn the camera parameters based on the input data. This allows the model to accurately infer 3D poses on unseen data by training on only a handful of samples from each synthesized setting and thus avoid the unbearable training cost increment. Another appealing feature of our method is that benefited from the capability of IRM in identifying the invariant features its performance on the seen camera settings is enhanced as well. Comprehensive experiments verify the superiority of our approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_PoseIRM_Enhance_3D_Human_Pose_Estimation_on_Unseen_Camera_Settings_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_PoseIRM_Enhance_3D_Human_Pose_Estimation_on_Unseen_Camera_Settings_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_PoseIRM_Enhance_3D_Human_Pose_Estimation_on_Unseen_Camera_Settings_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cai_PoseIRM_Enhance_3D_CVPR_2024_supplemental.zip | null |
Zero-Shot Structure-Preserving Diffusion Model for High Dynamic Range Tone Mapping | Ruoxi Zhu, Shusong Xu, Peiye Liu, Sicheng Li, Yanheng Lu, Dimin Niu, Zihao Liu, Zihao Meng, Zhiyong Li, Xinhua Chen, Yibo Fan | Tone mapping techniques aiming to convert high dynamic range (HDR) images to high-quality low dynamic range (LDR) images for display play a more crucial role in real-world vision systems with the increasing application of HDR images. However obtaining paired HDR and high-quality LDR images is difficult posing a challenge to deep learning based tone mapping methods. To overcome this challenge we propose a novel zero-shot tone mapping framework that utilizes shared structure knowledge allowing us to transfer a pre-trained mapping model from the LDR domain to HDR fields without paired training data. Our approach involves decomposing both the LDR and HDR images into two components: structural information and tonal information. To preserve the original image's structure we modify the reverse sampling process of a diffusion model and explicitly incorporate the structure information into the intermediate results. Additionally for improved image details we introduce a dual-control network architecture that enables different types of conditional inputs to control different scales of the output. Experimental results demonstrate the effectiveness of our approach surpassing previous state-of-the-art methods both qualitatively and quantitatively. Moreover our model exhibits versatility and can be applied to other low-level vision tasks without retraining. The code is available at https://github.com/ZSDM-HDR/Zero-Shot-Diffusion-HDR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Zero-Shot_Structure-Preserving_Diffusion_Model_for_High_Dynamic_Range_Tone_Mapping_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Zero-Shot_Structure-Preserving_Diffusion_Model_for_High_Dynamic_Range_Tone_Mapping_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Zero-Shot_Structure-Preserving_Diffusion_Model_for_High_Dynamic_Range_Tone_Mapping_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Zero-Shot_Structure-Preserving_Diffusion_CVPR_2024_supplemental.pdf | null |
VidLA: Video-Language Alignment at Scale | Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan, Son Tran, Benjamin Z. Yao, Belinda Zeng, Mubarak Shah, Trishul Chilimbi | In this paper we propose VidLA an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First they do not capture both short-range and long-range temporal dependencies and typically employ complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To effectively address this limitation we instead keep the network architecture simple and use a set of data tokens that operate at different temporal resolutions in a hierarchical manner accounting for the temporally hierarchical nature of videos. By employing a simple two-tower architecture we are able to initialize our video-language model with pretrained image-text foundation models thereby boosting the final performance. Second existing video-language alignment works struggle due to the lack of semantically aligned large-scale training data. To overcome it we leverage recent LLMs to curate the largest video-language dataset to date with better visual grounding. Furthermore unlike existing video-text datasets which only contain short clips our dataset is enriched with video clips of varying durations to aid our temporally hierarchical data tokens in extracting better representations at varying temporal scales. Overall empirical results show that our proposed approach surpasses state-of-the-art methods on multiple retrieval benchmarks especially on longer videos and performs competitively on classification benchmarks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Rizve_VidLA_Video-Language_Alignment_at_Scale_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.14870 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Rizve_VidLA_Video-Language_Alignment_at_Scale_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Rizve_VidLA_Video-Language_Alignment_at_Scale_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Rizve_VidLA_Video-Language_Alignment_CVPR_2024_supplemental.pdf | null |
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis | Linshan Wu, Jiaxin Zhuang, Hao Chen | Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However the lack of high-level semantics in pre-training still heavily hinders the performance of downstream tasks. We observe that 3D medical images contain relatively consistent contextual position information i.e. consistent geometric relations between different organs which leads to a potential way for us to learn consistent semantic representations in pre-training. In this paper we propose a simple-yet-effective Volume Contrast (VoCo) framework to leverage the contextual position priors for pre-training. Specifically we first generate a group of base crops from different regions while enforcing feature discrepancy among them where we employ them as class assignments of different regions. Then we randomly crop sub-volumes and predict them belonging to which class (located at which region) by contrasting their similarity to different base crops which can be seen as predicting contextual positions of different sub-volumes. Through this pretext task VoCo implicitly encodes the contextual position priors into model representations without the guidance of annotations enabling us to effectively improve the performance of downstream tasks that require high-level semantics. Extensive experimental results on six downstream tasks demonstrate the superior effectiveness of VoCo. Code will be available at https://github.com/Luffy03/VoCo. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_VoCo_A_Simple-yet-Effective_Volume_Contrastive_Learning_Framework_for_3D_Medical_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_VoCo_A_Simple-yet-Effective_Volume_Contrastive_Learning_Framework_for_3D_Medical_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_VoCo_A_Simple-yet-Effective_Volume_Contrastive_Learning_Framework_for_3D_Medical_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_VoCo_A_Simple-yet-Effective_CVPR_2024_supplemental.pdf | null |
CCEdit: Creative and Controllable Video Editing via Diffusion Models | Ruoyu Feng, Wenming Weng, Yanhui Wang, Yuhui Yuan, Jianmin Bao, Chong Luo, Zhibo Chen, Baining Guo | In this paper we present CCEdit a versatile generative video editing framework based on diffusion models. Our approach employs a novel trident network structure that separates structure and appearance control ensuring precise and creative editing capabilities. Utilizing the foundational ControlNet architecture we maintain the structural integrity of the video during editing. The incorporation of an additional appearance branch enables users to exert fine-grained control over the edited key frame. These two side branches seamlessly integrate into the main branch which is constructed upon existing text-to-image (T2I) generation models through learnable temporal layers. The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models as well as the option to provide the edited key frame. To facilitate comprehensive evaluation we introduce the BalanceCC benchmark dataset comprising 100 videos and 4 target prompts for each video. Our extensive user studies compare CCEdit with eight state-of-the-art video editing methods. The outcomes demonstrate CCEdit's substantial superiority over all other methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Feng_CCEdit_Creative_and_Controllable_Video_Editing_via_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.16496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Feng_CCEdit_Creative_and_Controllable_Video_Editing_via_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Feng_CCEdit_Creative_and_Controllable_Video_Editing_via_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Feng_CCEdit_Creative_and_CVPR_2024_supplemental.pdf | null |
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images | Yushuang Wu, Luyue Shi, Junhao Cai, Weihao Yuan, Lingteng Qiu, Zilong Dong, Liefeng Bo, Shuguang Cui, Xiaoguang Han | Generalizable 3D object reconstruction from single-view RGB-D images remains a challenging task particularly with real-world data. Current state-of-the-art methods develop Transformer-based implicit field learning necessitating an intensive learning paradigm that requires dense query-supervision uniformly sampled throughout the entire space. We propose a novel approach IPoD which harmonizes implicit field learning with point diffusion. This approach treats the query points for implicit field learning as a noisy point cloud for iterative denoising allowing for their dynamic adaptation to the target object shape. Such adaptive query points harness diffusion learning's capability for coarse shape recovery and also enhances the implicit representation's ability to delineate finer details. Besides an additional self-conditioning mechanism is designed to use implicit predictions as the guidance of diffusion learning leading to a cooperative system. Experiments conducted on the CO3D-v2 dataset affirm the superiority of IPoD achieving 7.8% improvement in F-score and 28.6% in Chamfer distance over existing methods. The generalizability of IPoD is also demonstrated on the MVImgNet dataset. Our project page is at https://yushuang-wu.github.io/IPoD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_IPoD_Implicit_Field_Learning_with_Point_Diffusion_for_Generalizable_3D_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_IPoD_Implicit_Field_Learning_with_Point_Diffusion_for_Generalizable_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_IPoD_Implicit_Field_Learning_with_Point_Diffusion_for_Generalizable_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_IPoD_Implicit_Field_CVPR_2024_supplemental.pdf | null |
HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained Images | Xihe Yang, Xingyu Chen, Daiheng Gao, Shaohui Wang, Xiaoguang Han, Baoyuan Wang | As for human avatar reconstruction contemporary techniques commonly necessitate the acquisition of costly data and struggle to achieve satisfactory results from a small number of casual images. In this paper we investigate this task from a few-shot unconstrained photo album. The reconstruction of human avatars from such data sources is challenging because of limited data amount and dynamic articulated poses. For handling dynamic data we integrate a skinning mechanism with deep marching tetrahedra (DMTet) to form a drivable tetrahedral representation which drives arbitrary mesh topologies generated by the DMTet for the adaptation of unconstrained images. To effectively mine instructive information from few-shot data we devise a two-phase optimization method with few-shot reference and few-shot guidance. The former focuses on aligning avatar identity with reference images while the latter aims to generate plausible appearances for unseen regions. Overall our framework called HaveFun can undertake avatar reconstruction rendering and animation. Extensive experiments on our developed benchmarks demonstrate that HaveFun exhibits substantially superior performance in reconstructing the human body and hand. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_HAVE-FUN_Human_Avatar_Reconstruction_from_Few-Shot_Unconstrained_Images_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_HAVE-FUN_Human_Avatar_Reconstruction_from_Few-Shot_Unconstrained_Images_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_HAVE-FUN_Human_Avatar_Reconstruction_from_Few-Shot_Unconstrained_Images_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_HAVE-FUN_Human_Avatar_CVPR_2024_supplemental.pdf | null |
ERMVP: Communication-Efficient and Collaboration-Robust Multi-Vehicle Perception in Challenging Environments | Jingyu Zhang, Kun Yang, Yilei Wang, Hanqi Wang, Peng Sun, Liang Song | Collaborative perception enhances perception performance by enabling autonomous vehicles to exchange complementary information. Despite its potential to revolutionize the mobile industry challenges in various environments such as communication bandwidth limitations localization errors and information aggregation inefficiencies hinder its implementation in practical applications. In this work we propose ERMVP a communication-Efficient and collaboration-Robust Multi-Vehicle Perception method in challenging environments. Specifically ERMVP has three distinct strengths: i) It utilizes the hierarchical feature sampling strategy to abstract a representative set of feature vectors using less communication overhead for efficient communication; ii) It employs the sparse consensus features to execute precise spatial location calibrations effectively mitigating the implications of vehicle localization errors; iii) A pioneering feature fusion and interaction paradigm is introduced to integrate holistic spatial semantics among different vehicles and data sources. To thoroughly validate our method we conduct extensive experiments on real-world and simulated datasets. The results demonstrate that the proposed ERMVP is significantly superior to the state-of-the-art collaborative perception methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_ERMVP_Communication-Efficient_and_Collaboration-Robust_Multi-Vehicle_Perception_in_Challenging_Environments_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_ERMVP_Communication-Efficient_and_Collaboration-Robust_Multi-Vehicle_Perception_in_Challenging_Environments_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_ERMVP_Communication-Efficient_and_Collaboration-Robust_Multi-Vehicle_Perception_in_Challenging_Environments_CVPR_2024_paper.html | CVPR 2024 | null | null |
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing | Kaiwen Zhang, Yifan Zhou, Xudong Xu, Bo Dai, Xingang Pan | Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However a notable limitation of diffusion models in comparison to GANs is their difficulty in smoothly interpolating between two image samples due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work we address this limitation via DiffMorpher an approach that enables smooth and natural image interpolation by harnessing the prior knowledge of a pre-trained diffusion model. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition where correspondence automatically emerges without the need for annotation. In addition we propose an attention interpolation and injection technique an adaptive normalization adjustment method and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories bridging a critical functional gap that distinguished diffusion models from GANs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_DiffMorpher_Unleashing_the_Capability_of_Diffusion_Models_for_Image_Morphing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_DiffMorpher_Unleashing_the_Capability_of_Diffusion_Models_for_Image_Morphing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_DiffMorpher_Unleashing_the_Capability_of_Diffusion_Models_for_Image_Morphing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_DiffMorpher_Unleashing_the_CVPR_2024_supplemental.pdf | null |
Towards Real-World HDR Video Reconstruction: A Large-Scale Benchmark Dataset and A Two-Stage Alignment Network | Yong Shu, Liquan Shen, Xiangyu Hu, Mengyao Li, Zihao Zhou | As an important and practical way to obtain high dynamic range (HDR) video HDR video reconstruction from sequences with alternating exposures is still less explored mainly due to the lack of large-scale real-world datasets. Existing methods are mostly trained on synthetic datasets which perform poorly in real scenes. In this work to facilitate the development of real-world HDR video reconstruction we present Real-HDRV a large-scale real-world benchmark dataset for HDR video reconstruction featuring various scenes diverse motion patterns and high-quality labels. Specifically our dataset contains 500 LDRs-HDRs video pairs comprising about 28000 LDR frames and 4000 HDR labels covering daytime nighttime indoor and outdoor scenes. To our best knowledge our dataset is the largest real-world HDR video reconstruction dataset. Correspondingly we propose an end-to-end network for HDR video reconstruction where a novel two-stage strategy is designed to perform alignment sequentially. Specifically the first stage performs global alignment with the adaptively estimated global offsets reducing the difficulty of subsequent alignment. The second stage implicitly performs local alignment in a coarse-to-fine manner at the feature level using the adaptive separable convolution. Extensive experiments demonstrate that: (1) models trained on our dataset can achieve better performance on real scenes than those trained on synthetic datasets; (2) our method outperforms previous state-of-the-art methods. Our dataset is available at https://github.com/yungsyu99/Real-HDRV. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shu_Towards_Real-World_HDR_Video_Reconstruction_A_Large-Scale_Benchmark_Dataset_and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.00244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shu_Towards_Real-World_HDR_Video_Reconstruction_A_Large-Scale_Benchmark_Dataset_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shu_Towards_Real-World_HDR_Video_Reconstruction_A_Large-Scale_Benchmark_Dataset_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shu_Towards_Real-World_HDR_CVPR_2024_supplemental.pdf | null |
Efficient 3D Implicit Head Avatar with Mesh-anchored Hash Table Blendshapes | Ziqian Bai, Feitong Tan, Sean Fanello, Rohit Pandey, Mingsong Dou, Shichen Liu, Ping Tan, Yinda Zhang | 3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However the computational cost of these methods remains a significant barrier to their widespread adoption particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fast neural rendering approaches for static scenes these methods cannot be simply employed to support realistic facial expressions such as in the case of a dynamic facial performance. To address these challenges we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality. Our key idea lies in the introduction of local hash table blendshapes which are learned and attached to the vertices of an underlying face parametric model. These per-vertex hash-tables are linearly merged with weights predicted via a CNN resulting in expression dependent embeddings. Our novel representation enables efficient density and color predictions using a lightweight MLP which is further accelerated by a hierarchical nearest neighbor search method. Extensive experiments show that our approach runs in real-time while achieving comparable rendering quality to state-of-the-arts and decent results on challenging expressions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bai_Efficient_3D_Implicit_Head_Avatar_with_Mesh-anchored_Hash_Table_Blendshapes_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01543 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_Efficient_3D_Implicit_Head_Avatar_with_Mesh-anchored_Hash_Table_Blendshapes_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_Efficient_3D_Implicit_Head_Avatar_with_Mesh-anchored_Hash_Table_Blendshapes_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bai_Efficient_3D_Implicit_CVPR_2024_supplemental.zip | null |
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks | Marina Neseem, Conor McCullough, Randy Hsin, Chas Leichner, Shan Li, In Suk Chong, Andrew Howard, Lukasz Lew, Sherief Reda, Ville-Mikko Rautio, Daniele Moro | Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in layers such as parameterized activation functions batch normalization and quantization scaling dominate the inference cost of low-precision models. These non-quantized elementwise operations are commonly overlooked in SOTA efficiency metrics such as Arithmetic Computation Effort (ACE). In this paper we propose ACEv2 - an extended version of ACE which offers a better alignment with the inference cost of quantized models and their energy consumption on ML hardware. Moreover we introduce PikeLPN a model that addresses these efficiency issues by applying quantization to both elementwise operations and multiply-accumulate operations. In particular we present a novel quantization technique for batch normalization layers named QuantNorm which allows for quantizing the batch normalization parameters without compromising the model performance. Additionally we propose applying Double Quantization where the quantization scaling parameters are quantized. Furthermore we recognize and resolve the issue of distribution mismatch in Separable Convolution layers by introducing Distribution-Heterogeneous Quantization which enables quantizing them to low-precision. PikeLPN achieves Pareto-optimality in efficiency-accuracy trade-off with up to 3X efficiency improvement compared to SOTA low-precision models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Neseem_PikeLPN_Mitigating_Overlooked_Inefficiencies_of_Low-Precision_Neural_Networks_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Neseem_PikeLPN_Mitigating_Overlooked_Inefficiencies_of_Low-Precision_Neural_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Neseem_PikeLPN_Mitigating_Overlooked_Inefficiencies_of_Low-Precision_Neural_Networks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Neseem_PikeLPN_Mitigating_Overlooked_CVPR_2024_supplemental.pdf | null |
CurveCloudNet: Processing Point Clouds with 1D Structure | Colton Stearns, Alex Fu, Jiateng Liu, Jeong Joon Park, Davis Rempe, Despoina Paschalidou, Leonidas J. Guibas | Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene resulting in a point cloud with notable 1D curve-like structures. In this work we introduce a new point cloud processing scheme and backbone called CurveCloudNet which takes advantage of the curve-like structure inherent to these sensors. While existing backbones discard the rich 1D traversal patterns and rely on generic 3D operations CurveCloudNet parameterizes the point cloud as a collection of polylines (dubbed a "curve cloud") establishing a local surface-aware ordering on the points. By reasoning along curves CurveCloudNet captures lightweight curve-aware priors to efficiently and accurately reason in several diverse 3D environments. We evaluate CurveCloudNet on multiple synthetic and real datasets that exhibit distinct 3D size and structure. We demonstrate that CurveCloudNet outperforms both point-based and sparse-voxel backbones in various segmentation settings notably scaling to large scenes better than point-based alternatives while exhibiting improved single-object performance over sparse-voxel alternatives. In all CurveCloudNet is an efficient and accurate backbone that can handle a larger variety of 3D environments than past works. | https://openaccess.thecvf.com/content/CVPR2024/papers/Stearns_CurveCloudNet_Processing_Point_Clouds_with_1D_Structure_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.12050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Stearns_CurveCloudNet_Processing_Point_Clouds_with_1D_Structure_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Stearns_CurveCloudNet_Processing_Point_Clouds_with_1D_Structure_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Stearns_CurveCloudNet_Processing_Point_CVPR_2024_supplemental.pdf | null |
CAGE: Controllable Articulation GEneration | Jiayi Liu, Hou In Ivan Tam, Ali Mahdavi-Amiri, Manolis Savva | We address the challenge of generating 3D articulated objects in a controllable fashion. Currently modeling articulated 3D objects is either achieved through laborious manual authoring or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape connectivity and motion using a denoising diffusion-based method with attention modules designed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category part shape and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation producing more realistic objects while conforming better to user constraints. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_CAGE_Controllable_Articulation_GEneration_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09570 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CAGE_Controllable_Articulation_GEneration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CAGE_Controllable_Articulation_GEneration_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_CAGE_Controllable_Articulation_CVPR_2024_supplemental.pdf | null |
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation | Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Jiaming Liu, Han Xiao, Chaoyou Fu, Hao Dong, Peng Gao | To reduce the reliance on large-scale datasets recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes and then evaluate their generalization performance on 'unseen' classes. However the prior pre-training stage not only introduces excessive time overhead but also incurs a significant domain gap on 'unseen' classes. To tackle these issues we propose a Non-parametric Network for few-shot 3D Segmentation Seg-NN and its Parametric variant Seg-PN. Without training Seg-NN extracts dense representations by hand-crafted filters and achieves comparable performance to existing parameterized models. Due to the elimination of pre-training Seg-NN can alleviate the domain gap issue and save a substantial amount of time. Based on Seg-NN Seg-PN only requires training a lightweight QUEry-Support Transferring (QUEST) module which enhances the interaction between the support set and query set. Experiments suggest that Seg-PN outperforms previous state-of-the-art method by +4.19% and +7.71% mIoU on S3DIS and ScanNet datasets respectively while reducing training time by -90% indicating its effectiveness and efficiency. Code is available https://github.com/yangyangyang127/Seg-NN. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_No_Time_to_Train_Empowering_Non-Parametric_Networks_for_Few-shot_3D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_No_Time_to_Train_Empowering_Non-Parametric_Networks_for_Few-shot_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_No_Time_to_Train_Empowering_Non-Parametric_Networks_for_Few-shot_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_No_Time_to_CVPR_2024_supplemental.pdf | null |
PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics | Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, Chenfanfu Jiang | We introduce PhysGaussian a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM) our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing marching cubes cage meshes or any other geometry embedding highlighting the principle of "what you see is what you simulate (WS^2)". Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities plastic metals non-Newtonian fluids and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_PhysGaussian_Physics-Integrated_3D_Gaussians_for_Generative_Dynamics_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.12198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PhysGaussian_Physics-Integrated_3D_Gaussians_for_Generative_Dynamics_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PhysGaussian_Physics-Integrated_3D_Gaussians_for_Generative_Dynamics_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_PhysGaussian_Physics-Integrated_3D_CVPR_2024_supplemental.pdf | null |
Spatio-Temporal Turbulence Mitigation: A Translational Perspective | Xingguang Zhang, Nicholas Chimitt, Yiheng Chi, Zhiyuan Mao, Stanley H. Chan | Recovering images distorted by atmospheric turbulence is a challenging inverse problem due to the stochastic nature of turbulence. Although numerous turbulence mitigation (TM) algorithms have been proposed their efficiency and generalization to real-world dynamic scenarios remain severely limited. Building upon the intuitions of classical TM algorithms we present the Deep Atmospheric TUrbulence Mitigation network (DATUM). DATUM aims to overcome major challenges when transitioning from classical to deep learning approaches. By carefully integrating the merits of classical multi-frame TM methods into a deep network structure we demonstrate that DATUM can efficiently perform long-range temporal aggregation using a recurrent fashion while deformable attention and temporal-channel attention seamlessly facilitate pixel registration and lucky imaging. With additional supervision tilt and blur degradation can be jointly mitigated. These inductive biases empower DATUM to significantly outperform existing methods while delivering a tenfold increase in processing speed. A large-scale training dataset ATSyn is presented as a co-invention to enable the generalization to real turbulence. Our code and datasets are available at https://xg416.github.io/DATUM/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Spatio-Temporal_Turbulence_Mitigation_A_Translational_Perspective_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.04244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Spatio-Temporal_Turbulence_Mitigation_A_Translational_Perspective_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Spatio-Temporal_Turbulence_Mitigation_A_Translational_Perspective_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Spatio-Temporal_Turbulence_Mitigation_CVPR_2024_supplemental.pdf | null |
FocusMAE: Gallbladder Cancer Detection from Ultrasound Videos with Focused Masked Autoencoders | Soumen Basu, Mayuna Gupta, Chetan Madan, Pankaj Gupta, Chetan Arora | In recent years automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection leveraging the inherent advantages of spatiotemporal representations. Employing the Masked Autoencoder (MAE) for representation learning we address shortcomings in conventional image-based methods. We propose a novel design called FocusMAE to systematically bias the selection of masking tokens from high-information regions fostering a more refined representation of malignancy. Additionally we contribute the most extensive US video dataset for GBC detection. We also note that this is the first study on US video-based GBC detection. We validate the proposed methods on the curated dataset and report a new SOTA accuracy of 96.4% for the GBC detection problem against an accuracy of 84% by current Image-based SOTA - GBCNet and RadFormer and 94.7% by Video-based SOTA - AdaMAE. We further demonstrate the generality of the proposed FocusMAE on a public CT-based Covid detection dataset reporting an improvement in accuracy by 3.3% over current baselines. Project page with source code trained models and data is available at: https://gbc-iitd.github.io/focusmae. | https://openaccess.thecvf.com/content/CVPR2024/papers/Basu_FocusMAE_Gallbladder_Cancer_Detection_from_Ultrasound_Videos_with_Focused_Masked_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.08848 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Basu_FocusMAE_Gallbladder_Cancer_Detection_from_Ultrasound_Videos_with_Focused_Masked_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Basu_FocusMAE_Gallbladder_Cancer_Detection_from_Ultrasound_Videos_with_Focused_Masked_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Basu_FocusMAE_Gallbladder_Cancer_CVPR_2024_supplemental.pdf | null |
Grounded Text-to-Image Synthesis with Attention Refocusing | Quynh Phung, Songwei Ge, Jia-Bin Huang | Driven by the scalable diffusion models trained on large-scale datasets text-to-image synthesis methods have shown compelling results. However these models still fail to precisely follow the text prompt involving multiple objects attributes or spatial compositions. In this paper we reveal the potential causes of the diffusion model's cross-attention and self-attention layers. We propose two novel losses to refocus attention maps according to a given spatial layout during sampling. Creating the layouts manually requires additional effort and can be tedious. Therefore we explore using large language models (LLM) to produce these layouts for our method. We conduct extensive experiments on the DrawBench HRS and TIFA benchmarks to evaluate our proposed method. We show that our proposed attention refocusing effectively improves the controllability of existing approaches. | https://openaccess.thecvf.com/content/CVPR2024/papers/Phung_Grounded_Text-to-Image_Synthesis_with_Attention_Refocusing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2306.05427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Phung_Grounded_Text-to-Image_Synthesis_with_Attention_Refocusing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Phung_Grounded_Text-to-Image_Synthesis_with_Attention_Refocusing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Phung_Grounded_Text-to-Image_Synthesis_CVPR_2024_supplemental.pdf | null |
OpenStreetView-5M: The Many Roads to Global Visual Geolocation | Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis, Constantin Aronssohn, Nacim Bouia, Stephanie Fu, Romain Loiseau, Van Nguyen Nguyen, Charles Raude, Elliot Vincent, Lintao Xu, Hongyu Zhou, Loic Landrieu | Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Yet the absence of standard large-scale open-access datasets with reliably localizable images has limited its potential. To address this issue we introduce OpenStreetView-5M a large-scale open-access dataset comprising over 5.1 million geo-referenced street view images covering 225 countries and territories. In contrast to existing benchmarks we enforce a strict train/test separation allowing us to evaluate the relevance of learned geographical features beyond mere memorization. To demonstrate the utility of our dataset we conduct an extensive benchmark of various state-of-the-art image encoders spatial representations and training strategies. All associated codes and models can be found at https://github.com/gastruc/osv5m. | https://openaccess.thecvf.com/content/CVPR2024/papers/Astruc_OpenStreetView-5M_The_Many_Roads_to_Global_Visual_Geolocation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Astruc_OpenStreetView-5M_The_Many_Roads_to_Global_Visual_Geolocation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Astruc_OpenStreetView-5M_The_Many_Roads_to_Global_Visual_Geolocation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Astruc_OpenStreetView-5M_The_Many_CVPR_2024_supplemental.pdf | null |
Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models | Matthew Kowal, Richard P. Wildes, Konstantinos G. Derpanis | Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models the Visual Concept Connectome (VCC) which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kowal_Visual_Concept_Connectome_VCC_Open_World_Concept_Discovery_and_their_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kowal_Visual_Concept_Connectome_VCC_Open_World_Concept_Discovery_and_their_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kowal_Visual_Concept_Connectome_VCC_Open_World_Concept_Discovery_and_their_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kowal_Visual_Concept_Connectome_CVPR_2024_supplemental.pdf | null |
IReNe: Instant Recoloring of Neural Radiance Fields | Alessio Mazzucchelli, Adrian Garcia-Garcia, Elena Garces, Fernando Rivas-Manzaneque, Francesc Moreno-Noguer, Adrian Penate-Sanchez | Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use lack precision at object boundaries and struggle to ensure multi-view consistency. We introduce IReNe to address these limitations enabling swift near real-time color editing in NeRF. Leveraging a pre-trained NeRF model and a single training image with user-applied color edits IReNe swiftly adjusts network parameters in seconds. This adjustment allows the model to generate new scene views accurately representing the color changes from the training image while also controlling object boundaries and view-specific effects. Object boundary control is achieved by integrating a trainable segmentation module into the model. The process gains efficiency by retraining only the weights of the last network layer. We observed that neurons in this layer can be classified into those responsible for view-dependent appearance and those contributing to diffuse appearance. We introduce an automated classification approach to identify these neuron types and exclusively fine-tune the weights of the diffuse neurons. This further accelerates training and ensures consistent color edits across different views. A thorough validation on a new dataset with edited object colors shows significant quantitative and qualitative advancements over competitors accelerating speeds by 5x and 500x. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mazzucchelli_IReNe_Instant_Recoloring_of_Neural_Radiance_Fields_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mazzucchelli_IReNe_Instant_Recoloring_of_Neural_Radiance_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mazzucchelli_IReNe_Instant_Recoloring_of_Neural_Radiance_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mazzucchelli_IReNe_Instant_Recoloring_CVPR_2024_supplemental.pdf | null |
Class Tokens Infusion for Weakly Supervised Semantic Segmentation | Sung-Hoon Yoon, Hoyong Kwon, Hyeonseong Kim, Kuk-Jin Yoon | Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT) the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token Infusion (CTI). By infusing the class tokens from images we guide class tokens to possess class-specific distinct characteristics and global-local consistency. For this we devise two kinds of token infusion: 1) Intra-image Class Token Infusion (I-CTI) and 2) Cross-Image Class Token Infusion (C-CTI). In I-CTI we infuse the class tokens from the same but differently augmented images and thus make CAMs consistent among various deformations (view color). In C-CTI by infusing the class tokens from the other images and imposing the resulting CAMs to be similar it learns class-specific distinct characteristics. Besides the CTI we bring the background (BG) concept into ViT with the BG token to reduce the false positive activation of CAMs. We demonstrate the effectiveness of our method on PASCAL VOC 2012 and MS COCO 2014 datasets achieving state-of-the-art results in weakly supervised semantic segmentation. The code is available at https://github.com/yoon307/CTI | https://openaccess.thecvf.com/content/CVPR2024/papers/Yoon_Class_Tokens_Infusion_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yoon_Class_Tokens_Infusion_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yoon_Class_Tokens_Infusion_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yoon_Class_Tokens_Infusion_CVPR_2024_supplemental.pdf | null |
FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | Yuxiang Lu, Suizhi Huang, Yuwen Yang, Shalayiding Sirejiding, Yue Ding, Hongtao Lu | Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks assuming model congruity that identical model architecture is deployed in each client. To relax this assumption and thus extend real-world applicability we introduce a novel problem setting Hetero-Client Federated Multi-Task Learning (HC-FMTL) to accommodate diverse task setups. The main challenge of HC-FMTL is the model incongruity issue that invalidates conventional aggregation methods. It also escalates the difficulties in model aggregation to deal with data and task heterogeneity inherent in FMTL. To address these challenges we propose the FedHCA^2 framework which allows for federated training of personalized models by modeling relationships among heterogeneous clients. Drawing on our theoretical insights into the difference between multi-task and federated optimization we propose the Hyper Conflict-Averse Aggregation scheme to mitigate conflicts during encoder updates. Additionally inspired by task interaction in MTL the Hyper Cross Attention Aggregation scheme uses layer-wise cross attention to enhance decoder interactions while alleviating model incongruity. Moreover we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates. Extensive experiments demonstrate the superior performance of FedHCA^2 in various HC-FMTL scenarios compared to representative methods. Code is available at https://github.com/innovator-zero/FedHCA2. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_FedHCA2_Towards_Hetero-Client_Federated_Multi-Task_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_FedHCA2_Towards_Hetero-Client_CVPR_2024_supplemental.pdf | null |
Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion | Xunpeng Yi, Han Xu, Hao Zhang, Linfeng Tang, Jiayi Ma | Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing with degradations in low-quality source images and non-interactive to multiple subjective and objective needs. To solve them we introduce a novel approach that leverages semantic text guidance image fusion model for degradation-aware and interactive image fusion task termed as Text-IF. It innovatively extends the classical image fusion to the text guided image fusion along with the ability to harmoniously address the degradation and interaction issues during fusion. Through the text semantic encoder and semantic interaction fusion decoder Text-IF is accessible to the all-in-one infrared and visible image degradation-aware processing and the interactive flexible fusion outcomes. In this way Text-IF achieves not only multi-modal image fusion but also multi-modal information fusion. Extensive experiments prove that our proposed text guided image fusion strategy has obvious advantages over SOTA methods in the image fusion performance and degradation treatment. The code is available at https://github.com/XunpengYi/Text-IF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yi_Text-IF_Leveraging_Semantic_Text_Guidance_for_Degradation-Aware_and_Interactive_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yi_Text-IF_Leveraging_Semantic_Text_Guidance_for_Degradation-Aware_and_Interactive_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yi_Text-IF_Leveraging_Semantic_Text_Guidance_for_Degradation-Aware_and_Interactive_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yi_Text-IF_Leveraging_Semantic_CVPR_2024_supplemental.pdf | null |
GRAM: Global Reasoning for Multi-Page VQA | Tsachi Blau, Sharon Fogel, Roi Ronen, Alona Golts, Roy Ganz, Elad Ben Avraham, Aviad Aberdam, Shahar Tsiper, Ron Litman | The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA) leading methods focus on the single-page setting while documents can span hundreds of pages. We present GRAM a method that seamlessly extends pre-trained single-page models to the multi-page setting without requiring computationally-heavy pretraining. To do so we leverage a single-page encoder for local page-level understanding and enhance it with document-level designated layers and learnable tokens facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens we propose a tailored bias adaptation method. For additional computational savings during decoding we introduce an optional compression stage using our compression-transformer (CFormer)reducing the encoded sequence length thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA demonstrating the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Blau_GRAM_Global_Reasoning_for_Multi-Page_VQA_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.03411 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Blau_GRAM_Global_Reasoning_for_Multi-Page_VQA_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Blau_GRAM_Global_Reasoning_for_Multi-Page_VQA_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Blau_GRAM_Global_Reasoning_CVPR_2024_supplemental.pdf | null |
MS-DETR: Efficient DETR Training with Mixed Supervision | Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, Jingdong Wang | DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth object. The traditional training procedure using one-to-one supervision in the original DETR lacks direct supervision for the object detection candidates. We aim at improving the DETR training efficiency by explicitly supervising the candidate generation procedure through mixing one-to-one supervision and one-to-many supervision. Our approach namely MS-DETR is simple and places one-to-many supervision to the object queries of the primary decoder that is used for inference. In comparison to existing DETR variants with one-to-many supervision such as Group DETR and Hybrid DETR our approach does not need additional decoder branches or object queries. The object queries of the primary decoder in our approach directly benefit from one-to-many supervision and thus are superior in object candidate prediction. Experimental results show that our approach outperforms related DETR variants such as DN-DETR Hybrid DETR and Group DETR and the combination with related DETR variants further improves the performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_MS-DETR_Efficient_DETR_Training_with_Mixed_Supervision_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_MS-DETR_Efficient_DETR_Training_with_Mixed_Supervision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_MS-DETR_Efficient_DETR_Training_with_Mixed_Supervision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_MS-DETR_Efficient_DETR_CVPR_2024_supplemental.pdf | null |
Learning to Produce Semi-dense Correspondences for Visual Localization | Khang Truong Giang, Soohwan Song, Sungho Jo | This study addresses the challenge of performing visual localization in demanding conditions such as night-time scenarios adverse weather and seasonal changes. While many prior studies have focused on improving image matching performance to facilitate reliable dense keypoint matching between images existing methods often heavily rely on predefined feature points on a reconstructed 3D model. Consequently they tend to overlook unobserved keypoints during the matching process. Therefore dense keypoint matches are not fully exploited leading to a notable reduction in accuracy particularly in noisy scenes. To tackle this issue we propose a novel localization method that extracts reliable semi-dense 2D-3D matching points based on dense keypoint matches. This approach involves regressing semi-dense 2D keypoints into 3D scene coordinates using a point inference network. The network utilizes both geometric and visual cues to effectively infer 3D coordinates for unobserved keypoints from the observed ones. The abundance of matching information significantly enhances the accuracy of camera pose estimation even in scenarios involving noisy or sparse 3D models. Comprehensive evaluations demonstrate that the proposed method outperforms other methods in challenging scenes and achieves competitive results in large-scale visual localization benchmarks. The code will be available at https://github.com/TruongKhang/DeViLoc | https://openaccess.thecvf.com/content/CVPR2024/papers/Giang_Learning_to_Produce_Semi-dense_Correspondences_for_Visual_Localization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.08359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Giang_Learning_to_Produce_Semi-dense_Correspondences_for_Visual_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Giang_Learning_to_Produce_Semi-dense_Correspondences_for_Visual_Localization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Giang_Learning_to_Produce_CVPR_2024_supplemental.pdf | null |
Amodal Ground Truth and Completion in the Wild | Guanqi Zhan, Chuanxia Zheng, Weidi Xie, Andrew Zisserman | This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for partially occluded objects in real images. This pipeline is used to construct an amodal completion evaluation benchmark MP3D-Amodal consisting of a variety of object categories and labels. To better handle the amodal completion task in the wild we explore two architecture variants: a two-stage model that first infers the occluder followed by amodal mask completion; and a one-stage model that exploits the representation power of Stable Diffusion for amodal segmentation across many categories. Without bells and whistles our method achieves a new state-of-the-art performance on Amodal segmentation datasets that cover a large variety of objects including COCOA and our new MP3D-Amodal dataset. The dataset model and code are available at https://www. robots.ox.ac.uk/ vgg/research/amodal/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhan_Amodal_Ground_Truth_and_Completion_in_the_Wild_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.17247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhan_Amodal_Ground_Truth_and_Completion_in_the_Wild_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhan_Amodal_Ground_Truth_and_Completion_in_the_Wild_CVPR_2024_paper.html | CVPR 2024 | null | null |
Motion Diversification Networks | Hee Jae Kim, Eshed Ohn-Bar | We introduce Motion Diversification Networks a novel framework for learning to generate realistic and diverse 3D human motion. Despite recent advances in deep generative motion modeling existing models often fail to produce samples that capture the full range of plausible and natural 3D human motion within a given context. The lack of diversity becomes even more apparent in applications where subtle and multi-modal 3D human forecasting is crucial for safety such as robotics and autonomous driving. Towards more realistic and functional 3D motion models we highlight limitations in existing generative modeling techniques particularly in overly simplistic latent code sampling strategies. We then introduce a transformer-based diversification mechanism that learns to effectively guide sampling in the latent space. Our proposed attention-based module queries multiple stochastic samples to flexibly predict a diverse set of latent codes which can be subsequently decoded into motion samples. The proposed framework achieves state-of-the-art diversity and accuracy prediction performance across a range of benchmarks and settings particularly when used to forecast intricate in-the-wild 3D human motion within complex urban environments. Our models datasets and code are available at https://mdncvpr.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Motion_Diversification_Networks_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Motion_Diversification_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Motion_Diversification_Networks_CVPR_2024_paper.html | CVPR 2024 | null | null |
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence | Junyi Zhang, Charles Herrmann, Junhwa Hur, Eric Chen, Varun Jampani, Deqing Sun, Ming-Hsuan Yang | While pre-trained large-scale vision models have shown significant promise for semantic correspondence their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset for both pre-training validating models. Our method achieves a [email protected] score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset outperforming the state of the art by 5.5p and 11.0p absolute gains respectively. Our code and datasets are publicly available at: https://telling-left-from-right.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Telling_Left_from_Right_Identifying_Geometry-Aware_Semantic_Correspondence_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Telling_Left_from_Right_Identifying_Geometry-Aware_Semantic_Correspondence_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Telling_Left_from_Right_Identifying_Geometry-Aware_Semantic_Correspondence_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Telling_Left_from_CVPR_2024_supplemental.pdf | null |
NECA: Neural Customizable Human Avatar | Junjin Xiao, Qing Zhang, Zhan Xu, Wei-Shi Zheng | Human avatar has become a novel type of 3D asset with various applications. Ideally a human avatar should be fully customizable to accommodate different settings and environments. In this work we introduce NECA an approach capable of learning versatile human representation from monocular or sparse-view videos enabling granular customization across aspects such as pose shadow shape lighting and texture. The core of our approach is to represent humans in complementary dual spaces and predict disentangled neural fields of geometry albedo shadow as well as an external lighting from which we are able to derive realistic rendering with high-frequency details via volumetric rendering. Extensive experiments demonstrate the advantage of our method over the state-of-the-art methods in photorealistic rendering as well as various editing tasks such as novel pose synthesis and relighting. Our code is available at https://github.com/iSEE-Laboratory/NECA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_NECA_Neural_Customizable_Human_Avatar_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_NECA_Neural_Customizable_Human_Avatar_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_NECA_Neural_Customizable_Human_Avatar_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_NECA_Neural_Customizable_CVPR_2024_supplemental.zip | null |
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object Detection | Wenjie Wang, Yehao Lu, Guangcong Zheng, Shuigen Zhan, Xiaoqing Ye, Zichang Tan, Jingdong Wang, Gaoang Wang, Xi Li | Vision-based roadside 3D object detection has attracted rising attention in autonomous driving domain since it encompasses inherent advantages in reducing blind spots and expanding perception range. While previous work mainly focuses on accurately estimating depth or height for 2D-to-3D mapping ignoring the position approximation error in the voxel pooling process. Inspired by this insight we propose a novel voxel pooling strategy to reduce such error dubbed BEVSpread. Specifically instead of bringing the image features contained in a frustum point to a single BEV grid BEVSpread considers each frustum point as a source and spreads the image features to the surrounding BEV grids with adaptive weights. To achieve superior propagation performance a specific weight function is designed to dynamically control the decay speed of the weights according to distance and depth. Aided by customized CUDA parallel acceleration BEVSpread achieves comparable inference time as the original voxel pooling. Extensive experiments on two large-scale roadside benchmarks demonstrate that as a plug-in BEVSpread can significantly improve the performance of existing frustum-based BEV methods by a large margin of (1.12 5.26 3.01) AP in vehicle pedestrian and cyclist. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_BEVSpread_Spread_Voxel_Pooling_for_Birds-Eye-View_Representation_in_Vision-based_Roadside_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_BEVSpread_Spread_Voxel_Pooling_for_Birds-Eye-View_Representation_in_Vision-based_Roadside_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_BEVSpread_Spread_Voxel_Pooling_for_Birds-Eye-View_Representation_in_Vision-based_Roadside_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_BEVSpread_Spread_Voxel_CVPR_2024_supplemental.pdf | null |
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection | Chengjie Wang, Wenbing Zhu, Bin-Bin Gao, Zhenye Gan, Jiangning Zhang, Zhihao Gu, Shuguang Qian, Mingang Chen, Lizhuang Ma | Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on mainstream datasets such as MVTec and the differences of methods cannot be well distinguished leading to a significant gap between public datasets and actual application scenarios. On the other hand the research on various new practical anomaly detection settings is limited by the scale of the dataset posing a risk of overfitting in evaluation results. Therefore we propose a large-scale Real-world and multi-view Industrial Anomaly Detection dataset named Real-IAD which contains 150K high-resolution images of 30 different objects an order of magnitude larger than existing datasets. It has a larger range of defect area and ratio proportions making it more challenging than previous datasets. To make the dataset closer to real application scenarios we adopted a multi-view shooting method and proposed sample-level evaluation metrics. In addition beyond the general unsupervised anomaly detection setting we propose a new setting for Fully Unsupervised Industrial Anomaly Detection (FUIAD) based on the observation that the yield rate in industrial production is usually greater than 60% which has more practical application value. Finally we report the results of popular IAD methods on the Real-IAD dataset providing a highly challenging benchmark to promote the development of the IAD field. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Real-IAD_A_Real-World_Multi-View_Dataset_for_Benchmarking_Versatile_Industrial_Anomaly_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Real-IAD_A_Real-World_Multi-View_Dataset_for_Benchmarking_Versatile_Industrial_Anomaly_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Real-IAD_A_Real-World_Multi-View_Dataset_for_Benchmarking_Versatile_Industrial_Anomaly_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Real-IAD_A_Real-World_CVPR_2024_supplemental.pdf | null |
PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor | Vidit Goel, Elia Peruzzo, Yifan Jiang, Dejia Xu, Xingqian Xu, Nicu Sebe, Trevor Darrell, Zhangyang Wang, Humphrey Shi | Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-level image editing. In this work we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing free-form shape editing adding objects and variations. Thanks to our design we do not require any inversion step. Additionally we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Goel_PAIR_Diffusion_A_Comprehensive_Multimodal_Object-Level_Image_Editor_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Goel_PAIR_Diffusion_A_Comprehensive_Multimodal_Object-Level_Image_Editor_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Goel_PAIR_Diffusion_A_Comprehensive_Multimodal_Object-Level_Image_Editor_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Goel_PAIR_Diffusion_A_CVPR_2024_supplemental.pdf | null |
Boosting Adversarial Transferability by Block Shuffle and Rotation | Kunyu Wang, Xuanran He, Wenxuan Wang, Xiaosen Wang | Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability which refers to their ability to deceive other models thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability the performance still falls short compared with white-box attacks. In this work we observe that existing input transformation based attacks one of the mainstream transfer-based attacks result in different attention heatmaps on various models which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically BSR splits the input image into several blocks then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability which significantly outperforms the state-of-the-art methods. Code is available at https://github.com/Trustworthy-AI-Group/BSR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Boosting_Adversarial_Transferability_by_Block_Shuffle_and_Rotation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.10299 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Boosting_Adversarial_Transferability_by_Block_Shuffle_and_Rotation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Boosting_Adversarial_Transferability_by_Block_Shuffle_and_Rotation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Boosting_Adversarial_Transferability_CVPR_2024_supplemental.pdf | null |
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving | Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, Liping Jing, Yiming Nie, Bin Dai | Vision-centric autonomous driving has recently raised wide attention due to its lower cost. Pre-training is essential for extracting a universal representation. However current vision-centric pre-training typically relies on either 2D or 3D pre-text tasks overlooking the temporal characteristics of autonomous driving as a 4D scene understanding task. In this paper we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework dubbed DriveWorld which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion. Specifically we propose a Memory State-Space Model for spatio-temporal modelling which consists of a Dynamic Memory Bank module for learning temporal-aware latent dynamics to predict future changes and a Static Scene Propagation module for learning spatial-aware latent statics to offer comprehensive scene contexts. We additionally introduce a Task Prompt to decouple task-aware features for various downstream tasks. The experiments demonstrate that DriveWorld delivers promising results on various autonomous driving tasks. When pre-trained with the OpenScene dataset DriveWorld achieves a 7.5% increase in mAP for 3D object detection a 3.0% increase in IoU for online mapping a 5.0% increase in AMOTA for multi-object tracking a 0.1m decrease in minADE for motion forecasting a 3.0% increase in IoU for occupancy prediction and a 0.34m reduction in average L2 error for planning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Min_DriveWorld_4D_Pre-trained_Scene_Understanding_via_World_Models_for_Autonomous_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.04390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Min_DriveWorld_4D_Pre-trained_Scene_Understanding_via_World_Models_for_Autonomous_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Min_DriveWorld_4D_Pre-trained_Scene_Understanding_via_World_Models_for_Autonomous_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Min_DriveWorld_4D_Pre-trained_CVPR_2024_supplemental.pdf | null |
Bridging the Gap Between End-to-End and Two-Step Text Spotting | Mingxin Huang, Hongliang Li, Yuliang Liu, Xiang Bai, Lianwen Jin | Modularity plays a crucial role in the development and maintenance of complex systems. While end-to-end text spotting efficiently mitigates the issues of error accumulation and sub-optimal performance seen in traditional two-step methodologies the two-step methods continue to be favored in many competitions and practical settings due to their superior modularity. In this paper we introduce Bridging Text Spotting a novel approach that resolves the error accumulation and suboptimal performance issues in two-step methods while retaining modularity. To achieve this we adopt a well-trained detector and recognizer that are developed and trained independently and then lock their parameters to preserve their already acquired capabilities. Subsequently we introduce a Bridge that connects the locked detector and recognizer through a zero-initialized neural network. This zero-initialized neural network initialized with weights set to zeros ensures seamless integration of the large receptive field features in detection into the locked recognizer. Furthermore since the fixed detector and recognizer cannot naturally acquire end-to-end optimization features we adopt the Adapter to facilitate their efficient learning of these features. We demonstrate the effectiveness of the proposed method through extensive experiments: Connecting the latest detector and recognizer through Bridging Text Spotting we achieved an accuracy of 83.3% on Total-Text 69.8% on CTW1500 and 89.5% on ICDAR 2015. The code is available at https://github.com/mxin262/Bridging-Text-Spotting. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Bridging_the_Gap_Between_End-to-End_and_Two-Step_Text_Spotting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04624 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_Bridging_the_Gap_Between_End-to-End_and_Two-Step_Text_Spotting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_Bridging_the_Gap_Between_End-to-End_and_Two-Step_Text_Spotting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_Bridging_the_Gap_CVPR_2024_supplemental.pdf | null |
TokenCompose: Text-to-Image Diffusion with Token-level Supervision | Zirui Wang, Zhizhou Sha, Zheng Ding, Yilin Wang, Zhuowen Tu | We present TokenCompose a Latent Diffusion Model for text-to-image generation that achieves enhanced consistency between user-specified text prompts and model-generated images. Despite its tremendous success the standard denoising process in the Latent Diffusion Model takes text prompts as conditions only absent explicit constraint for the consistency between the text prompts and the image contents leading to unsatisfactory results for composing multiple object categories. Our proposed TokenCompose aims to improve multi-category instance composition by introducing the token-wise consistency terms between the image content and object segmentation maps in the finetuning stage. TokenCompose can be applied directly to the existing training pipeline of text-conditioned diffusion models without extra human labeling information. By finetuning Stable Diffusion with our approach the model exhibits significant improvements in multi-category instance composition and enhanced photorealism for its generated images. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_TokenCompose_Text-to-Image_Diffusion_with_Token-level_Supervision_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_TokenCompose_Text-to-Image_Diffusion_with_Token-level_Supervision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_TokenCompose_Text-to-Image_Diffusion_with_Token-level_Supervision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_TokenCompose_Text-to-Image_Diffusion_CVPR_2024_supplemental.pdf | null |
SUGAR: Pre-training 3D Visual Representations for Robotics | Shizhe Chen, Ricardo Garcia, Ivan Laptev, Cordelia Schmid | Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet prevailing approaches focus on pre-training 2D representations being sub-optimal to deal with occlusions and accurately localize objects in complex 3D scenes. Meanwhile 3D representation learning has been limited to single-object understanding. To address these limitations we introduce a novel 3D pre-training framework for robotics named SUGAR that captures semantic geometric and affordance properties of objects through 3D point clouds. We underscore the importance of cluttered scenes in 3D representation learning and automatically construct a multi-object dataset benefiting from cost-free supervision in simulation. SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks namely cross-modal knowledge distillation for semantic learning masked point modeling to understand geometry structures grasping pose synthesis for object affordance 3D instance segmentation and referring expression grounding to analyze cluttered scenes. We evaluate our learned representation on three robotic-related tasks namely zero-shot 3D object recognition referring expression grounding and language-driven robotic manipulation. Experimental results show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SUGAR_Pre-training_3D_Visual_Representations_for_Robotics_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SUGAR_Pre-training_3D_Visual_Representations_for_Robotics_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SUGAR_Pre-training_3D_Visual_Representations_for_Robotics_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_SUGAR_Pre-training_3D_CVPR_2024_supplemental.pdf | null |
LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes | Shanlin Sun, Bingbing Zhuang, Ziyu Jiang, Buyu Liu, Xiaohui Xie, Manmohan Chandraker | Photorealistic simulation plays a crucial role in applications such as autonomous driving where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First our framework learns a geometric scene representation from Lidar which are fused with the implicit grid-based representation for radiance decoding thereby supplying stronger geometric information offered by explicit point cloud. Second we put forth a robust occlusion-aware depth supervision scheme which allows utilizing densified Lidar points by accumulation. Third we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_LidaRF_Delving_into_Lidar_for_Neural_Radiance_Field_on_Street_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.00900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_LidaRF_Delving_into_Lidar_for_Neural_Radiance_Field_on_Street_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_LidaRF_Delving_into_Lidar_for_Neural_Radiance_Field_on_Street_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_LidaRF_Delving_into_CVPR_2024_supplemental.zip | null |
PairAug: What Can Augmented Image-Text Pairs Do for Radiology? | Yutong Xie, Qi Chen, Sinuo Wang, Minh-Son To, Iris Lee, Ee Win Khoo, Kerolos Hendy, Daniel Koh, Yong Xia, Qi Wu | Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity however most augmentation methods exhibit a limited focus prioritising either image or text augmentation exclusively. Acknowledging this limitation our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug concurrently expanding both image and text data substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at https://github.com/YtongXie/PairAug. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_PairAug_What_Can_Augmented_Image-Text_Pairs_Do_for_Radiology_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PairAug_What_Can_Augmented_Image-Text_Pairs_Do_for_Radiology_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_PairAug_What_Can_Augmented_Image-Text_Pairs_Do_for_Radiology_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_PairAug_What_Can_CVPR_2024_supplemental.pdf | null |
FINER: Flexible Spectral-bias Tuning in Implicit NEural Representation by Variable-periodic Activation Functions | Zhen Liu, Hao Zhu, Qi Zhang, Jingde Fu, Weibing Deng, Zhan Ma, Yanwen Guo, Xun Cao | Implicit Neural Representation (INR) which utilizes a neural network to map coordinate inputs to corresponding attributes is causing a revolution in the field of signal processing. However current INR techniques suffer from a restricted capability to tune their supported frequency set resulting in imperfect performance when representing complex signals with multiple frequencies. We have identified that this frequency-related problem can be greatly alleviated by introducing variable-periodic activation functions for which we propose FINER. By initializing the bias of the neural network within different ranges sub-functions with various frequencies in the variable-periodic function are selected for activation. Consequently the supported frequency set of FINER can be flexibly tuned leading to improved performance in signal representation. We demonstrate the capabilities of FINER in the contexts of 2D image fitting 3D signed distance field representation and 5D neural radiance fields optimization and we show that it outperforms existing INRs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_FINER_Flexible_Spectral-bias_Tuning_in_Implicit_NEural_Representation_by_Variable-periodic_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_FINER_Flexible_Spectral-bias_Tuning_in_Implicit_NEural_Representation_by_Variable-periodic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_FINER_Flexible_Spectral-bias_Tuning_in_Implicit_NEural_Representation_by_Variable-periodic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_FINER_Flexible_Spectral-bias_CVPR_2024_supplemental.pdf | null |
Harnessing Large Language Models for Training-free Video Anomaly Detection | Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, Elisa Ricci | Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision one-class supervision or in an unsupervised setting. Training-based methods are prone to be domain-specific thus being costly for practical deployment as any domain change will involve data collection and model training. In this paper we radically depart from previous efforts and propose LAnguage-based VAD (LAVAD) a method tackling VAD in a novel training-free paradigm exploiting the capabilities of pre-trained large language models (LLMs) and existing vision-language models (VLMs). We leverage VLM-based captioning models to generate textual descriptions for each frame of any test video. With the textual scene description we then devise a prompting mechanism to unlock the capability of LLMs in terms of temporal aggregation and anomaly score estimation turning LLMs into an effective video anomaly detector. We further leverage modality-aligned VLMs and propose effective techniques based on cross-modal similarity for cleaning noisy captions and refining the LLM-based anomaly scores. We evaluate LAVAD on two large datasets featuring real-world surveillance scenarios (UCF-Crime and XD-Violence) showing that it outperforms both unsupervised and one-class methods without requiring any training or data collection. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zanella_Harnessing_Large_Language_Models_for_Training-free_Video_Anomaly_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zanella_Harnessing_Large_Language_Models_for_Training-free_Video_Anomaly_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zanella_Harnessing_Large_Language_Models_for_Training-free_Video_Anomaly_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zanella_Harnessing_Large_Language_CVPR_2024_supplemental.pdf | null |
TextCraftor: Your Text Encoder Can be Image Quality Controller | Yanyu Li, Xian Liu, Anil Kag, Ju Hu, Yerlan Idelbayev, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov, Jian Ren | Diffusion-based text-to-image generative models e.g. Stable Diffusion have revolutionized the field of content generation enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities these models are not without their limitations. It is still challenging to synthesize an image that aligns well with the input text and multiple runs with carefully crafted prompts are required to achieve satisfactory results. To mitigate these limitations numerous studies have endeavored to fine-tune the pre-trained diffusion models i.e.. UNet utilizing various technologies. Yet amidst these efforts a pivotal question of text-to-image diffusion model training has remained largely unexplored: Is it possible and feasible to fine-tune the text encoder to improve the performance of text-to-image diffusion models? Our findings reveal that instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models we can enhance it through our proposed fine-tuning approach TextCraftor leading to substantial improvements in quantitative benchmarks and human assessments. Interestingly our technique also empowers controllable image generation through the interpolation of different text encoders fine-tuned with various rewards. We also demonstrate that TextCraftor is orthogonal to UNet finetuning and can be combined to further improve generative quality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_TextCraftor_Your_Text_Encoder_Can_be_Image_Quality_Controller_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18978 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_TextCraftor_Your_Text_Encoder_Can_be_Image_Quality_Controller_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_TextCraftor_Your_Text_Encoder_Can_be_Image_Quality_Controller_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_TextCraftor_Your_Text_CVPR_2024_supplemental.pdf | null |
FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment | Jinglin Xu, Sibo Yin, Guohao Zhao, Zishuo Wang, Yuxin Peng | Existing action quality assessment (AQA) methods mainly learn deep representations at the video level for scoring diverse actions. Due to the lack of a fine-grained understanding of actions in videos they harshly suffer from low credibility and interpretability thus insufficient for stringent applications such as Olympic diving events. We argue that a fine-grained understanding of actions requires the model to perceive and parse actions in both time and space which is also the key to the credibility and interpretability of the AQA technique. Based on this insight we propose a new fine-grained spatial-temporal action parser named FineParser. It learns human-centric foreground action representations by focusing on target action regions within each frame and exploiting their fine-grained alignments in time and space to minimize the impact of invalid backgrounds during the assessment. In addition we construct fine-grained annotations of human-centric foreground action masks for the FineDiving dataset called FineDiving-HM. With refined annotations on diverse target action procedures FineDiving-HM can promote the development of real-world AQA systems. Through extensive experiments we demonstrate the effectiveness of FineParser which outperforms state-of-the-art methods while supporting more tasks of fine-grained action understanding. Data and code are available at https://github.com/PKU-ICST-MIPL/FineParser_CVPR2024. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_FineParser_A_Fine-grained_Spatio-temporal_Action_Parser_for_Human-centric_Action_Quality_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.06887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FineParser_A_Fine-grained_Spatio-temporal_Action_Parser_for_Human-centric_Action_Quality_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FineParser_A_Fine-grained_Spatio-temporal_Action_Parser_for_Human-centric_Action_Quality_CVPR_2024_paper.html | CVPR 2024 | null | null |
Video Recognition in Portrait Mode | Mingfei Han, Linjie Yang, Xiaojie Jin, Jiashi Feng, Xiaojun Chang, Heng Wang | The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With the growing popularity of smartphones and social media applications recognizing portrait mode videos is becoming increasingly important. To this end we have developed the first dataset dedicated to portrait mode video recognition namely PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a data-driven manner comprising 400 fine-grained categories and rigorous quality assurance was implemented to ensure the accuracy of human annotations. In addition to the new dataset we conducted a comprehensive analysis of the impact of video format (portrait mode versus landscape mode) on recognition accuracy and spatial bias due to the different formats. Furthermore we designed extensive experiments to explore key aspects of portrait mode video recognition including the choice of data augmentation evaluation procedure the importance of temporal information and the role of audio modality. Building on the insights from our experimental results and the introduction of PortraitMode-400 our paper aims to inspire further research efforts in this emerging research area. | https://openaccess.thecvf.com/content/CVPR2024/papers/Han_Video_Recognition_in_Portrait_Mode_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.13746 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Video_Recognition_in_Portrait_Mode_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Video_Recognition_in_Portrait_Mode_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Han_Video_Recognition_in_CVPR_2024_supplemental.pdf | null |
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model | Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, Wei-Shi Zheng | Universal image restoration is a practical and potential computer vision task for real-world applications. The main challenge of this task is handling the different degradation distributions at once. Existing methods mainly utilize task-specific conditions (e.g. prompt) to guide the model to learn different distributions separately named multi-partite mapping. However it is not suitable for universal model learning as it ignores the shared information between different tasks. In this work we propose an advanced selective hourglass mapping strategy based on diffusion model termed DiffUIR. Two novel considerations make our DiffUIR non-trivial. Firstly we equip the model with strong condition guidance to obtain accurate generation direction of diffusion model (selective). More importantly DiffUIR integrates a flexible shared distribution term (SDT) into the diffusion algorithm elegantly and naturally which gradually maps different distributions into a shared one. In the reverse process combined with SDT and strong condition guidance DiffUIR iteratively guides the shared distribution to the task-specific distribution with high image quality (hourglass). Without bells and whistles by only modifying the mapping strategy we achieve state-of-the-art performance on five image restoration tasks 22 benchmarks in the universal setting and zero-shot generalization setting. Surprisingly by only using a lightweight model (only 0.89M) we could achieve outstanding performance. The source code and pre-trained models are available at https://github.com/iSEE-Laboratory/DiffUIR | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_Selective_Hourglass_Mapping_for_Universal_Image_Restoration_Based_on_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Selective_Hourglass_Mapping_for_Universal_Image_Restoration_Based_on_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Selective_Hourglass_Mapping_for_Universal_Image_Restoration_Based_on_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_Selective_Hourglass_Mapping_CVPR_2024_supplemental.pdf | null |
Language Models as Black-Box Optimizers for Vision-Language Models | Shihong Liu, Samuel Yu, Zhiqiu Lin, Deepak Pathak, Deva Ramanan | Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However many VLMs rely on proprietary data and are not open-source which restricts the use of white-box approaches for fine-tuning. As such we aim to develop a black-box approach to optimize VLMs through natural language prompts thereby avoiding the need to access model parameters feature embeddings or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Language_Models_as_Black-Box_Optimizers_for_Vision-Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.05950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Language_Models_as_Black-Box_Optimizers_for_Vision-Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Language_Models_as_Black-Box_Optimizers_for_Vision-Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Language_Models_as_CVPR_2024_supplemental.pdf | null |
Exploring Orthogonality in Open World Object Detection | Zhicheng Sun, Jinghan Li, Yadong Mu | Open world object detection aims to identify objects of unseen categories and incrementally recognize them once their annotations are provided. In distinction to the traditional paradigm that is limited to predefined categories this setting promises a continual and generalizable way of estimating objectness using class-agnostic information. However achieving such decorrelation between objectness and class information proves challenging. Without explicit consideration existing methods usually exhibit low recall on unknown objects and can misclassify them into known classes. To address this problem we exploit three levels of orthogonality in the detection process: First the objectness and classification heads are disentangled by operating on separate sets of features that are orthogonal to each other in a devised polar coordinate system. Secondly a prediction decorrelation loss is introduced to guide the detector towards more general and class-independent prediction. Furthermore we propose a calibration scheme that helps maintain orthogonality throughout the training process to mitigate catastrophic interference and facilitate incremental learning of previously unseen objects. Our method is comprehensively evaluated on open world and incremental object detection benchmarks demonstrating its effectiveness in detecting both known and unknown objects. Code and models are available at https://github.com/feifeiobama/OrthogonalDet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_Exploring_Orthogonality_in_Open_World_Object_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Exploring_Orthogonality_in_Open_World_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Exploring_Orthogonality_in_Open_World_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Exploring_Orthogonality_in_CVPR_2024_supplemental.pdf | null |
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding | Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, Lidong Bing | Large Vision-Language Models (LVLMs) have advanced considerably intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success LVLMs still suffer from the issue of object hallucinations where models generate plausible yet incorrect outputs that include objects that do not exist in the images. To mitigate this issue we introduce Visual Contrastive Decoding (VCD) a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors two essential causes of object hallucinations. This adjustment ensures the generated content is closely grounded to visual inputs resulting in contextually accurate outputs. Our experiments show that VCD without either additional training or the usage of external tools significantly mitigates the object hallucination issue across different LVLM families. Beyond mitigating object hallucinations VCD also excels in general LVLM benchmarks highlighting its wide-ranging applicability. | https://openaccess.thecvf.com/content/CVPR2024/papers/Leng_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_through_Visual_Contrastive_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16922 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Leng_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_through_Visual_Contrastive_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Leng_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_through_Visual_Contrastive_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Leng_Mitigating_Object_Hallucinations_CVPR_2024_supplemental.pdf | null |
IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation | Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, Daniel Aliaga | Generative object compositing emerges as a promising new avenue for compositional image editing. However the requirement of object identity preservation poses a significant challenge limiting practical usage of most existing methods. In response this paper introduces IMPRINT a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic identity-preserving pretraining of the object encoder enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Song_IMPRINT_Generative_Object_Compositing_by_Learning_Identity-Preserving_Representation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10701 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Song_IMPRINT_Generative_Object_Compositing_by_Learning_Identity-Preserving_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Song_IMPRINT_Generative_Object_Compositing_by_Learning_Identity-Preserving_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Song_IMPRINT_Generative_Object_CVPR_2024_supplemental.pdf | null |
Audio-Visual Segmentation via Unlabeled Frame Exploitation | Jinxiang Liu, Yikun Liu, Fei Zhang, Chen Ju, Ya Zhang, Yanfeng Wang | Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed we experimentally reveal that current methods reach marginal performance gain within the use of the unlabeled frames leading to the underutilization issue. To fully explore the potential of the unlabeled frames for AVS we explicitly divide them into two categories based on their temporal characteristics i.e. neighboring frame (NF) and distant frame (DF). NFs temporally adjacent to the labeled frame often contain rich motion information that assists in the accurate localization of sounding objects. Contrary to NFs DFs have long temporal distances from the labeled frame which share semantic-similar objects with appearance variations. Considering their unique characteristics we propose a versatile framework that effectively leverages them to tackle AVS. Specifically for NFs we exploit the motion cues as the dynamic guidance to improve the objectness localization. Besides we exploit the semantic cues in DFs by treating them as valid augmentations to the labeled frames which are then used to enrich data diversity in a self-training manner. Extensive experimental results demonstrate the versatility and superiority of our method unleashing the power of the abundant unlabeled frames. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Audio-Visual_Segmentation_via_Unlabeled_Frame_Exploitation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11074 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Audio-Visual_Segmentation_via_Unlabeled_Frame_Exploitation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Audio-Visual_Segmentation_via_Unlabeled_Frame_Exploitation_CVPR_2024_paper.html | CVPR 2024 | null | null |
DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos | Arjun Balasingam, Joseph Chandler, Chenning Li, Zhoutong Zhang, Hari Balakrishnan | This paper presents DriveTrack a new benchmark and data generation framework for long-range keypoint tracking in real-world videos. DriveTrack is motivated by the observation that the accuracy of state-of-the-art trackers depends strongly on visual attributes around the selected keypoints such as texture and lighting. The problem is that these artifacts are especially pronounced in real-world videos but these trackers are unable to train on such scenes due to a dearth of annotations. DriveTrack bridges this gap by building a framework to automatically annotate point tracks on autonomous driving datasets. We release a dataset consisting of 1 billion point tracks across 24 hours of video which is seven orders of magnitude greater than prior real-world benchmarks and on par with the scale of synthetic benchmarks. DriveTrack unlocks new use cases for point tracking in real-world videos. First we show that fine-tuning keypoint trackers on DriveTrack improves accuracy on real-world scenes by up to 7%. Second we analyze the sensitivity of trackers to visual artifacts in real scenes and motivate the idea of running assistive keypoint selectors alongside trackers. | https://openaccess.thecvf.com/content/CVPR2024/papers/Balasingam_DriveTrack_A_Benchmark_for_Long-Range_Point_Tracking_in_Real-World_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09523 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Balasingam_DriveTrack_A_Benchmark_for_Long-Range_Point_Tracking_in_Real-World_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Balasingam_DriveTrack_A_Benchmark_for_Long-Range_Point_Tracking_in_Real-World_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Balasingam_DriveTrack_A_Benchmark_CVPR_2024_supplemental.zip | null |
Infrared Adversarial Car Stickers | Xiaopei Zhu, Yuqiu Liu, Zhanhao Hu, Jianmin Li, Xiaolin Hu | Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work we propose a physical attack method against infrared detectors based on 3D modeling which is applied to a real car. The goal is to design a set of infrared adversarial stickers to make cars invisible to infrared detectors at various viewing angles distances and scenes. We build a 3D infrared car model with real infrared characteristics and propose an infrared adversarial pattern generation method based on 3D mesh shadow. We propose a 3D control points-based mesh smoothing algorithm and use a set of smoothness loss functions to enhance the smoothness of adversarial meshes and facilitate the sticker implementation. Besides We designed the aluminum stickers and conducted physical experiments on two real Mercedes-Benz A200L cars. Our adversarial stickers hid the cars from Faster RCNN an object detector at various viewing angles distances and scenes. The attack success rate (ASR) was 91.49% for real cars. In comparison the ASRs of random stickers and no sticker were only 6.21% and 0.66% respectively. In addition the ASRs of the designed stickers against six unseen object detectors such as YOLOv3 and Deformable DETR were between 73.35%-95.80% showing good transferability of the attack performance across detectors. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Infrared_Adversarial_Car_Stickers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.09924 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Infrared_Adversarial_Car_Stickers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Infrared_Adversarial_Car_Stickers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Infrared_Adversarial_Car_CVPR_2024_supplemental.zip | null |
Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior | Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu | Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Sculpt3D_Multi-View_Consistent_Text-to-3D_Generation_with_Sparse_3D_Prior_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.09140 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Sculpt3D_Multi-View_Consistent_Text-to-3D_Generation_with_Sparse_3D_Prior_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Sculpt3D_Multi-View_Consistent_Text-to-3D_Generation_with_Sparse_3D_Prior_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Sculpt3D_Multi-View_Consistent_CVPR_2024_supplemental.pdf | null |
FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions | Jiong Wang, Fengyu Yang, Bingliang Li, Wenbo Gou, Danqi Yan, Ailing Zeng, Yijun Gao, Junle Wang, Yanqing Jing, Ruimao Zhang | Estimating the 3D structure of the human body from nat- ural scenes is a fundamental aspect of visual perception. 3D human pose estimation is a vital step in advancing fields like AIGC and human-robot interaction serving as a crucial tech- nique for understanding and interacting with human actions in real-world settings. However the current datasets often collected under single laboratory conditions using complex motion capture equipment and unvarying backgrounds are insufficient. The absence of datasets on variable conditions is stalling the progress of this crucial task. To facilitate the development of 3D pose estimation we present FreeMan the first large-scale multi-view dataset collected under the real- world conditions. FreeMan was captured by synchronizing 8 smartphones across diverse scenarios. It comprises 11M frames from 8000 sequences viewed from different perspec- tives. These sequences cover 40 subjects across 10 different scenarios each with varying lighting conditions. We have also established an semi-automated pipeline containing er- ror detection to reduce the workload of manual check and ensure precise annotation. We provide comprehensive eval- uation baselines for a range of tasks underlining the sig- nificant challenges posed by FreeMan. Further evaluations of standard indoor/outdoor human sensing datasets reveal that FreeMan offers robust representation transferability in real and complex scenes. FreeMan is publicly available at https://wangjiongw.github.io/freeman. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_FreeMan_Towards_Benchmarking_3D_Human_Pose_Estimation_under_Real-World_Conditions_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.05073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_FreeMan_Towards_Benchmarking_3D_Human_Pose_Estimation_under_Real-World_Conditions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_FreeMan_Towards_Benchmarking_3D_Human_Pose_Estimation_under_Real-World_Conditions_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_FreeMan_Towards_Benchmarking_CVPR_2024_supplemental.pdf | null |
ScanFormer: Referring Expression Comprehension by Iteratively Scanning | Wei Su, Peihan Miao, Huanzhang Dou, Xi Li | Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance they perform a dense perception of images which incorporates redundant visual regions unrelated to linguistic queries leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks with limited exploration in vision-language fields. To address this we propose a coarse-to-fine iterative perception framework called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration irrelevant patches are discarded by our designed informativeness prediction. Furthermore we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets namely RefCOCO RefCOCO+ RefCOCOg and ReferItGame verify the effectiveness of our method which can strike a balance between accuracy and efficiency. | https://openaccess.thecvf.com/content/CVPR2024/papers/Su_ScanFormer_Referring_Expression_Comprehension_by_Iteratively_Scanning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Su_ScanFormer_Referring_Expression_Comprehension_by_Iteratively_Scanning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Su_ScanFormer_Referring_Expression_Comprehension_by_Iteratively_Scanning_CVPR_2024_paper.html | CVPR 2024 | null | null |
Model Inversion Robustness: Can Transfer Learning Help? | Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran, Ngoc-Bao Nguyen, Ngai-Man Cheung | Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performance posing serious threats to privacy. Meanwhile all existing MI defense methods rely on regularization that is in direct conflict with the training objective resulting in noticeable degradation in model utility. In this work we take a different perspective and propose a novel and simple Transfer Learning-based Defense against Model Inversion (TL-DMI) to render MI-robust models. Particularly by leveraging TL we limit the number of layers encoding sensitive information from private training dataset thereby degrading the performance of MI attack. We conduct an analysis using Fisher Information to justify our method. Our defense is remarkably simple to implement. Without bells and whistles we show in extensive experiments that TL-DMI achieves state-of-the-art (SOTA) MI robustness. Our code pre-trained models demo and inverted data are available at: https://hosytuyen.github.io/projects/TL-DMI | https://openaccess.thecvf.com/content/CVPR2024/papers/Ho_Model_Inversion_Robustness_Can_Transfer_Learning_Help_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.05588 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ho_Model_Inversion_Robustness_Can_Transfer_Learning_Help_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ho_Model_Inversion_Robustness_Can_Transfer_Learning_Help_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ho_Model_Inversion_Robustness_CVPR_2024_supplemental.pdf | null |
Portrait4D: Learning One-Shot 4D Head Avatar Synthesis using Synthetic Data | Yu Deng, Duomin Wang, Xiaohang Ren, Xingyu Chen, Baoyuan Wang | Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data. A novel learning strategy is enforced to enhance the generalizability to real images by disentangling the learning process of 3D reconstruction and reenactment. Experiments demonstrate our superiority over the prior art. | https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_Portrait4D_Learning_One-Shot_4D_Head_Avatar_Synthesis_using_Synthetic_Data_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Portrait4D_Learning_One-Shot_4D_Head_Avatar_Synthesis_using_Synthetic_Data_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Portrait4D_Learning_One-Shot_4D_Head_Avatar_Synthesis_using_Synthetic_Data_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_Portrait4D_Learning_One-Shot_CVPR_2024_supplemental.pdf | null |
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding | Hao Li, Dingwen Zhang, Yalun Dai, Nian Liu, Lechao Cheng, Jingfeng Li, Jingdong Wang, Junwei Han | Applying Neural Radiance Fields (NeRF) to downstream perception tasks for scene understanding and representation is becoming increasingly popular. Most existing methods treat semantic prediction as an additional rendering task i.e. the "label rendering" task to build semantic NeRFs. However by rendering semantic/instance labels per pixel without considering the contextual information of the rendered image these methods usually suffer from unclear boundary segmentation and abnormal segmentation of pixels within an object. To solve this problem we propose Generalized Perception NeRF (GP-NeRF) a novel pipeline that makes the widely used segmentation model and NeRF work compatibly under a unified framework for facilitating context-aware 3D scene perception. To accomplish this goal we introduce transformers to aggregate radiance as well as semantic embedding fields jointly for novel views and facilitate the joint volumetric rendering of both fields. In addition we propose two self-distillation mechanisms i.e. the Semantic Distill Loss and the Depth-Guided Semantic Distill Loss to enhance the discrimination and quality of the semantic field and the maintenance of geometric consistency. In evaluation as shown in Fig. 1 we conduct experimental comparisons under two perception tasks (i.e. semantic and instance segmentation) using both synthetic and real-world datasets. Notably our method outperforms SOTA approaches by 6.94% 11.76% and 8.47% on generalized semantic segmentation finetuning semantic segmentation and instance segmentation respectively | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_GP-NeRF_Generalized_Perception_NeRF_for_Context-Aware_3D_Scene_Understanding_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_GP-NeRF_Generalized_Perception_NeRF_for_Context-Aware_3D_Scene_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_GP-NeRF_Generalized_Perception_NeRF_for_Context-Aware_3D_Scene_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_GP-NeRF_Generalized_Perception_CVPR_2024_supplemental.pdf | null |
Polarization Wavefront Lidar: Learning Large Scene Reconstruction from Polarized Wavefronts | Dominik Scheuble, Chenyang Lei, Seung-Hwan Baek, Mario Bijelic, Felix Heide | Lidar has become a cornerstone sensing modality for 3D vision especially for large outdoor scenarios and autonomous driving. Conventional lidar sensors are capable of providing centimeter-accurate distance information by emitting laser pulses into a scene and measuring the time-of-flight (ToF) of the reflection. However the polarization of the received light that depends on the surface orientation and material properties is usually not considered. As such the polarization modality has the potential to improve scene reconstruction beyond distance measurements. In this work we introduce a novel long-range polarization wavefront lidar sensor (PolLidar) that modulates the polarization of the emitted and received light. Departing from conventional lidar sensors PolLidar allows access to the raw time-resolved polarimetric wavefronts. We leverage polarimetric wavefronts to estimate normals distance and material properties in outdoor scenarios with a novel learned reconstruction method. To train and evaluate the method we introduce a simulated and real-world long-range dataset with paired raw lidar data ground truth distance and normal maps. We find that the proposed method improves normal and distance reconstruction by 53% mean angular error and 41% mean absolute error compared to existing shape-from-polarization (SfP) and ToF methods. Code and data are open-sourced here. | https://openaccess.thecvf.com/content/CVPR2024/papers/Scheuble_Polarization_Wavefront_Lidar_Learning_Large_Scene_Reconstruction_from_Polarized_Wavefronts_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Scheuble_Polarization_Wavefront_Lidar_Learning_Large_Scene_Reconstruction_from_Polarized_Wavefronts_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Scheuble_Polarization_Wavefront_Lidar_Learning_Large_Scene_Reconstruction_from_Polarized_Wavefronts_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Scheuble_Polarization_Wavefront_Lidar_CVPR_2024_supplemental.pdf | null |
GDA: Generalized Diffusion for Robust Test-time Adaptation | Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, Cheng-Hao Kuo | Machine learning models face generalization challenges when exposed to out-of-distribution (OOD) samples with unforeseen distribution shifts. Recent research reveals that for vision tasks test-time adaptation employing diffusion models can achieve state-of-the-art accuracy improvements on OOD samples by generating domain-aligned samples without altering the model's weights. Unfortunately those studies have primarily focused on pixel-level corruptions thereby lacking the generalization to adapt to a broader range of OOD types. We introduce Generalized Diffusion Adaptation (GDA) a novel diffusion-based test-time adaptation method robust against diverse OOD types. Specifically GDA iteratively guides the diffusion by applying a marginal entropy loss derived from the model in conjunction with style and content preservation losses during the reverse sampling process. In other words GDA considers the model's output behavior and the samples' semantic information as a whole reducing ambiguity in downstream tasks. based adaptation. Evaluation across various model architectures and OOD benchmarks indicates that GDA consistently surpasses previous diffusion-based adaptation methods. Notably it achieves the highest classification accuracy improvements ranging from 4.4% to 5.02% on ImageNet-C and 2.5% to 7.4% on Rendition Sketch and Stylized benchmarks. This performance highlights GDA's generalization to a broader range of OOD benchmarks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tsai_GDA_Generalized_Diffusion_for_Robust_Test-time_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tsai_GDA_Generalized_Diffusion_for_Robust_Test-time_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tsai_GDA_Generalized_Diffusion_for_Robust_Test-time_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tsai_GDA_Generalized_Diffusion_CVPR_2024_supplemental.pdf | null |
ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis | Muhammad Hamza Mughal, Rishabh Dabral, Ikhsanul Habibie, Lucia Donatelli, Marc Habermann, Christian Theobalt | Gestures play a key role in human communication. Recent methods for co-speech gesture generation while managing to generate beat-aligned motions struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal semantically coherent gestures require modeling the complex interactions between the language and human motion and can be controlled by focusing on certain words. Therefore we present ConvoFusion a diffusion-based approach for multi-modal gesture synthesis which can not only generate gestures based on multi-modal speech inputs but can also facilitate controllability in gesture synthesis. Our method proposes two guidance objectives that allow the users to modulate the impact of different conditioning modalities (e.g. audio vs text) as well as to choose certain words to be emphasized during gesturing. Our method is versatile in that it can be trained either for generating monologue gestures or even the conversational gestures. To further advance the research on multi-party interactive gestures the DnD Group Gesture dataset is released which contains 6 hours of gesture data showing 5 people interacting with one another. We compare our method with several recent works and demonstrate effectiveness of our method on a variety of tasks. We urge the reader to watch our supplementary video at https://vcai.mpi-inf.mpg.de/projects/ConvoFusion/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mughal_ConvoFusion_Multi-Modal_Conversational_Diffusion_for_Co-Speech_Gesture_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.17936 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mughal_ConvoFusion_Multi-Modal_Conversational_Diffusion_for_Co-Speech_Gesture_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mughal_ConvoFusion_Multi-Modal_Conversational_Diffusion_for_Co-Speech_Gesture_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mughal_ConvoFusion_Multi-Modal_Conversational_CVPR_2024_supplemental.zip | null |
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