[Project Page] | [Arxiv] | [Code]
We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the vast 3D data corpus of Objaverse-1.0 and 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 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. We also release the metadata of all successfully rendered objects from objaverse-xl's Github subset in meta_xl_tot.csv. For customized blender from objaverse-xl, please use the curated xl data list objaverseXL_curated_uuid_list.txt.
For text-to-4D generation, the captions are obtained from the work Cap3D.
If you find this repository/work/dataset helpful in your research, please consider citing the paper and starring the repo ⭐.
@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}
}
[Project Page] | [Arxiv] | [Code]
We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the vast 3D data corpus of Objaverse-1.0 and 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 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. We also release the metadata of all successfully rendered objects from objaverse-xl's Github subset in meta_xl_tot.csv. For customized blender from objaverse-xl, please use the curated xl data list objaverseXL_curated_uuid_list.txt.
For text-to-4D generation, the captions are obtained from the work Cap3D.
If you find this repository/work/dataset helpful in your research, please consider citing the paper and starring the repo ⭐.
@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}
}