update dataset descriptions; release metadata

README.md

@@ -2,26 +2,30 @@
 
 The official implementation of paper "Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models".
 
-[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Video (narrated)]](https://www.youtube.com/watch?v=9q8SV1Xf_Xw) | [[Video (results)]](https://www.youtube.com/watch?v=gXVoPTGb734) | [[Arxiv]](https://arxiv.org/abs/2403.16993)
+[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Video (narrated)]](https://www.youtube.com/watch?v=9q8SV1Xf_Xw) | [[Video (results)]](https://www.youtube.com/watch?v=gXVoPTGb734) | [[Arxiv]](https://arxiv.org/abs/2403.16993) | [[Dataset]](https://huggingface.co/datasets/hw-liang/Diffusion4D)
 
 ## News
 
 - 2024.5.28:  Released data preparation code!
+- 2024.5.27:  Released metadata for objects!
 - 2024.5.26:  Released on arxiv!
 
 
 
 # Preparation of 4D data
-We curate 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 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 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. (To be uploaded) Rendered 4D images by our team to save you GPU time.
 
-## 4D Dataset ID
-We curated a total of 54K dynamic 3D assets from Objaverse-1.0 and Objaverse-xl. With objaverse-1.0, we provide the selected 11K ids in `rendering/src/ObjV1_curated.txt`. Uncurated 42K ids of all the animated cases in objaverse-1.0 is in `rendering/src/ObjV1_all_animated.txt`
+## 4D Dataset ID/Metadata
+We collect a total of 54K dynamic 3D assets from Objaverse-1.0 and Objaverse-xl. 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 from objaverse-xl can be found in [huggingface](https://huggingface.co/datasets/hw-liang/Diffusion4D). We also release the metadata of all successfully rendered objects from objaverse-xl's Github subset.
+
 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 coming soon!
+More about the dataset and curation scripts are coming soon!
 
 ## 4D Dataset Rendering Script
 1. Clone the repository and enter the rendering directory:
@@ -120,4 +124,4 @@ This project is based on numerous outstanding research efforts and open-source c
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