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---
license: apache-2.0
task_categories:
- text-to-3d
- image-to-3d
language:
- en
tags:
- 4d
- 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/) | [[Code]](https://github.com/VITA-Group/Diffusion4D) |
## News
- 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:
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 your GPU time.
## 4D Dataset ID/Metadata
We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k).
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!
## Citation
If you find this repository/work/dataset helpful in your research, please consider citing the paper and starring the [repo](https://github.com/VITA-Group/Diffusion4D) ⭐.
```
@article{liang2024diffusion4d,
title={Diffusion4D: Fast Spatial-temporal Consistent
4D Generation via Video Diffusion Models},
author={},
journal={arXiv preprint arXiv:},
year={2024}
}
``` |