Update README.md

README.md

@@ -17,28 +17,25 @@ size_categories:
 
 # Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
 
-[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Code]](https://github.com/VITA-Group/Diffusion4D) | 
+[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Arxiv]](https://arxiv.org/abs/2405.16645) | [[Code]](https://github.com/VITA-Group/Diffusion4D)
 
 ## News
 - 2024.6.4:  Released rendered data from curated [objaverse-1.0](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverse1.0_curated), including orbital videos of dynamic 3D, orbital videos of static 3D, and monocular videos from front view.
 - 2024.5.27: Released metadata for objects!
 
 ## Overview
-
-We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the
-vast 3D data corpus of [Objaverse-1.0](https://objaverse.allenai.org/objaverse-1.0/) and [Objaverse-XL](https://github.com/allenai/objaverse-xl). We apply a series of empirical rules to filter the dataset. You can find more details in our paper. In this part, we will release the selected 4D assets, including:
+We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the vast 3D data corpus of [Objaverse-1.0](https://objaverse.allenai.org/objaverse-1.0/) and [Objaverse-XL](https://github.com/allenai/objaverse-xl). We apply a series of empirical rules to filter the dataset. You can find more details in our paper. In this part, we will release the selected 4D assets, including:
 1. Selected high-quality 4D object ID.
 2. A render script using Blender, providing optional settings to render your personalized data.
-3. Rendered 4D images by our team to save your GPU time.
+3. Rendered 4D images by our team to save your GPU time. With 8 GPUs and a total of 16 threads, it took 5.5 days to render the curated objaverse-1.0 dataset.
 
 ## 4D Dataset ID/Metadata
-We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k).
+We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k). Then we curate a high-quality subset to train our models. 
 
 Metadata of animated objects (323k) from objaverse-xl can be found in [meta_xl_animation_tot.csv](https://huggingface.co/datasets/hw-liang/Diffusion4D/blob/main/meta_xl_animation_tot.csv). 
 We also release the metadata of all successfully rendered objects from objaverse-xl's Github subset in [meta_xl_tot.csv](https://huggingface.co/datasets/hw-liang/Diffusion4D/blob/main/meta_xl_tot.csv).
 
-For text-to-4D generation, the captions are obtained from the work [Cap3D](https://huggingface.co/datasets/tiange/Cap3D). 
-More about the dataset and curation scripts are coming soon!
+For text-to-4D generation, the captions are obtained from the work [Cap3D](https://huggingface.co/datasets/tiange/Cap3D).
 
 
 ## Citation