| | --- |
| | 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/) | [[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: |
| | 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. |
| |
|
| | ## 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. |
| |
|
| | 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). |
| |
|
| |
|
| | ## 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={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}, |
| | year={2024} |
| | } |
| | ``` |
