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README.md

@@ -10,33 +10,34 @@ tags:
 - 3d
 - text-to-4d
 - image-to-4d
-- 3d-to-4d
 size_categories:
 - 1M<n<10M
 ---
 
 # Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
 
-[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Arxiv]](https://arxiv.org/abs/2405.16645) | [[Code]](https://github.com/VITA-Group/Diffusion4D)
+[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Code]](https://github.com/VITA-Group/Diffusion4D) | 
 
 ## News
-- 2024.6.28:  Released rendered data from curated [objaverse-xl](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverseXL_curated).
-- 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. With 8 GPUs and a total of 16 threads, it took 5.5 days to render the curated objaverse-1.0 dataset.
+3. (To be uploaded) Rendered 4D images by our team to save your GPU time.
 
 ## 4D Dataset ID/Metadata
-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. 
+We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k). We curate a high-quality subset to train our models. With objaverse-1.0, we provide the selected 11K ids in `rendering/src/ObjV1_curated.txt`. Uncurated 42k IDs of all the animated objects from objaverse-1.0 are in `rendering/src/ObjV1_all_animated.txt`.
 
 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).
+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!
 
 
 ## Citation
@@ -45,9 +46,10 @@ If you find this repository/work/dataset helpful in your research, please consid
 
 ```
 @article{liang2024diffusion4d,
-  title={Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models},
-  author={Liang, Hanwen and Yin, Yuyang and Xu, Dejia and Liang, Hanxue and Wang, Zhangyang and Plataniotis, Konstantinos N and Zhao, Yao and Wei, Yunchao},
-  journal={arXiv preprint arXiv:2405.16645},
+  title={Diffusion4D: Fast Spatial-temporal Consistent
+    4D Generation via Video Diffusion Models},
+  author={},
+  journal={arXiv preprint arXiv:},
   year={2024}
 }
-```
+```

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