Mask-Guided Video Generation: Enhancing Motion Control and Quality with Limited Data
Diffusion models;video generation
Yes
No ethics review needed.
1. Please compare the proposed method with the state-of-the-art methods such as CogVideoX, Open Sora, etc. 
2. Discussion with the related methods.
3. Discussion on the practical value of this work. It seems that better results can be attained by the existing methods.
+ The introduction of mask guidance in video generation.
+ First-frame sharing is adopted to enhance the temporal consistency.
+ Incremental generation is leveraged for generating long videos.
This paper presented a mask-guided video generation method, which can be trained efficiently on a single GPU. First-frame sharing is adopted to enhance the temporal consistency, while incremental generation is leveraged for generating long videos. Experiments are carried out to evaluate the proposed method.
- The technical contribution is not insufficient. Maybe first-frame sharing is new to video generation, but it cannot achieve competing performance in comparison with existing open-sourced video generation models such as CogVideoX, Open Sora, etc. As for mask guidance, similar idea has been suggested in ControlNet for conditional image generation. Incremental generation is also suggested in StreamingT2V [1] StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text, https://arxiv.org/abs/2403.14773.
- The performance may be inferior to the state-of-the-art video generation methods.
Yes
No ethics review needed.
Question: Does the model need to be trained separately for each motion pattern?
++ The integration of mask condition for masked attention mechanism improves the performance of generated videos.

++ The paper is well written.
This paper proposes the mask-guided video generation to introduce foreground masks for learning region-specific attention. This method first generates the first frame using ControlNet, and allows for incrementally generation of longer video sequences with motion masks conditions.
-- The method relies on providing motion masks during inference, which limits its practicality for real-world applications. How to get the motion masks for arbitrary videos? And how robust is the proposed method towards inaccurate masks?

-- The method requires the first frame to be generated first using ControlNet, and then "animate" the first frame with motion mask sequence. However, such a pipeline faces significant challenges to generate videos with complex effects such as changing illuminations, generation with a variable number of subjects, etc. The experiments in this paper also mainly show single subject videos and only one video with multiple birds, especially the single horse running prompt. How could the method be extended to more complex scenarios such as varying illuminations? 

-- There is no video results to directly compare the temporal consistency of generated videos. It would be better to provide more video comparisons in supplementary materials. 

-- There is no quantitative results in the ablation study, and it remains unclear how many text prompts are used for the ablation study. It is difficult to analyse the effectiveness of the proposed design. Therefore, it is suggested to report quantitative metrics for ablation study such as FID and clearly clarify the number and complexity of prompts used for ablation study.
Yes
No ethics review needed.
1. The authors are suggested to cover a more comprehensive discussion of existing methods and reorganize the related works section on text-to-video generation by splitting it into two parts: few-shot/zero-shot motion learning and mask-guided generation. This structure would provide an important context for understanding the novelty and positioning of this paper.
1. The showcased videos have high quality, benefiting from the pre-trained text-to-image generation model.
2. This paper is technically clear. It provides detailed descriptions of the proposed method and implementation details.
This paper introduces a mask-guided video generation approach. By integrating mask sequences that isolate the foreground object and employing a first-frame sharing strategy, the model can control object motion and ensure stability across frames. The method requires retraining a new model on each set of videos of new motion concepts.
1. The task setting in this paper is very weird. Training a video generation model on a small set of videos with a shared motion concept has already been addressed in prior works like Tune-A-Video and LAMP. This paper’s approach differs by introducing masks as a strong constraint to guide video generation, which significantly limits the diversity of generated videos. Consequently, users must not only train a new model for each video set but also should supply a mask sequence, combining the drawbacks of few-shot training and controllable video generation. This approach requires computing resources to retrain new models, with the generated video diversity strictly constrained by the provided mask sequence. It is suggested to provide a more detailed comparison of computational requirements and generated diversity between the proposed method and prior approaches, such as Tune-A-Video and LAMP.

2. It is difficult to attribute the final quality of motions in the generated videos to either the model's learned motion concept or the reduced search space resulting from the mask sequence, which could be driving motion stability. The lack of an ablation study on this point leaves the source of these improvements unclear. It is suggested to add specific ablation experiments that would help isolate the contributions of the learned motion concept versus the mask sequence constraints.  Adding specific ablation experiments to isolate the contributions of the learned motion concept from the constraints imposed by the mask sequence is recommended.

3. The technical contributions of this paper are limited, as it merely combines LAMP with mask-guided generation in a straightforward manner. Few-shot motion learning and mask-guided controllable generation have already been extensively explored in prior works. 

4. The experimental results and analysis are quite limited. The quality of motion generation is a crucial aspect, yet there is a limited analysis of this in the experiments, and quantitative results, including those in Table 1, lack any evaluation specific to motion. Furthermore, mask guidance is a core component of the proposed method that significantly influences the generated outcomes, but the proposed method is not compared against other mask-guided approaches. It is suggested to add a metric that focuses on the motion quality. The proposed method is suggested to be compared with some mask-guided methods, like FateZero.


[1] Qi, Chenyang, et al. "Fatezero: Fusing attentions for zero-shot text-based video editing." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
Yes
No ethics review needed.
1. Why do we need mask-aware video generation? How to get diverse results when the mask is provided.
2. Where is the video demo?
3. There is only a visual ablation of this method in the paper, what about the numerical results in the larger scale?
- Mask-guided is novel for me as a video generation condition.
- This method requires only a small dataset for training.
This paper presents an image-to-video generation method with mask guidance. Thanks to this design, the method can generate video with very limited data. This method needs frame-specific masks for training and testing. The method also has a first frame-sharing noise method to enable better temporal consistency and a mask-aware attention model. This method compares several zero-shot/one-shot methods.
- I am curious about the motivation behind this paper. The mask is hard to generate when inference, which makes this method impractical.
- There are no video results for this paper.
- The comparison methods are too old to evaluate the performance of this method.
- Generating videos using small datasets from stable diffusion (text-to-image model) is out of fashion. Current state-of-the-art methods directly generate videos from large-scale training.
Our model efficiently utilizes resources and achieves controllability and consistency in video generation through motion sequences obtained from drawings or extracted masks.
@inproceedings{
anonymous2024maskguided,
title={Mask-Guided Video Generation: Enhancing Motion Control and Quality with Limited Data},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=9GNTtaIZh6},
note={under review}
}
Recent advancements in diffusion models have brought new vitality into visual content creation. However, current text-to-video generation models still face challenges such as high training costs, substantial data requirements, and difficulties in maintaining consistency between given text and motion of the foreground object. To address these challenges, we propose mask-guided video generation, which requires only a small amount of data and is trained on a single GPU. Furthermore, to mitigate the impact of background interference on controllable text-to-video generation, we utilize mask  sequences obtained through drawing or extraction, along with the first-frame content, to guide video generation. Specifically, our model introduces foreground masks into existing architectures to learn region-specific attention, precisely matching text features and the motion of the foreground object.  Subsequently, video generation is guided by the mask sequences to prevent the sudden disappearance of foreground objects. Our model also incorporates a first-frame sharing strategy during inference, leading to better stability in the video generation. Additionally, our approach allows for incrementally  generation of longer video sequences. By employing this method, our model achieves efficient resource utilization and ensures controllability and consistency in video generation using mask sequences. Extensive qualitative and quantitative experiments demonstrate that this approach excels in various video generation tasks, such as video editing and generating artistic videos, outperforming previous methods in terms of consistency and quality.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Diffusion models video generation
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/7c432419930262ab73467f66c7cf0ae95aa0d7c6.pdf
generative models
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Mask-Guided Video Generation: Enhancing Motion Control and Quality with Limited Data
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission