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Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models

The official implementation of work "Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models".

[Project Page] | [Arxiv] | [Video (Youtube)] | [视频 (Bilibili)] | [Huggingface Dataset]

Image-to-4D

Text-to-4D

3D-to-4D

News

• 2024.6.28: Released rendered data from curated objaverse-xl, including orbital videos of dynamic 3D and monocular videos from front view.
• 2024.6.4: Released rendered data from curated objaverse-1.0, including orbital videos of dynamic 3D, orbital videos of static 3D, and monocular videos from front view.
• 2024.5.27: Released metadata for objects and data preparation code!
• 2024.5.26: Released on arxiv!

4D Dataset Preparation

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 curate the source dataset. You can find more details in our paper. In this part, we will release the selected 4D assets, including:

1. Curated high-quality 4D object ID.
2. A render script using Blender, providing optional settings to render your personalized data.
3. Rendered objaverse-1.0 4D images and Rendered objaverse-xl 4D images by our team to save you GPU time. With 8 GPUs and a total of 16 threads, it took 5.5 days to render the curated objaverse-1.0 dataset and about 30 days for objaverse-xl dataset.

4D Dataset ID/Metadata

We first 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.

The uncurated 42k IDs of all the animated objects from objaverse-1.0 are in rendering/src/ObjV1_all_animated.txt. The curated ~11k IDs of the animated objects from objaverse-1.0 are in rendering/src/ObjV1_curated.txt.

Metadata of animated objects (323k) from objaverse-xl can be found in huggingface. 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.

4D Dataset Rendering Script

1. Clone the repository and enter the rendering directory:

git clone https://github.com/VITA-Group/Diffusion4D.git && \
cd rendering

2. Download Blender:

wget https://download.blender.org/release/Blender3.2/blender-3.2.2-linux-x64.tar.xz && \
tar -xf blender-3.2.2-linux-x64.tar.xz && \
rm blender-3.2.2-linux-x64.tar.xz

3. Download 4D objects

pip install objaverse
python download.py --id_path src/sample.txt

Please change objaverse._VERSIONED_PATH in download.py to the path you prefer to store the glb files. By default, it will be downloaded to obj_v1/.

4. Render 4D images

python render.py --obj_path "./obj_v1/glbs" \
                --save_dir './output' \
                --gpu_num 8           \
                --frame_num 24        \
                --azimuth_aug  1      \
                --elevation_aug 0     \
                --resolution 256      \
                --mode_multi 1        \
                --mode_static 1       \
                --mode_front_view 0   \
                --mode_four_view 0

Script Explanation:

• --obj_path Downloaded object path in step 3. Keep the same as your 'BASE_PATH'.
• --save_dir Directory to save.
• --gpu_num GPU number for rendering.
• --frame_num Number of frames to render. E.g., 24 means render from 'time=0' to 'time=24' images. You can set more or fewer frames, but the motion stops at a certain timestep, which differs with each case. Therefore, we do not recommend setting a large number of frames.
• --azimuth_aug If set to 1, use azimuth augmentation. Images will be rendered from a random azimuth. Otherwise, set to 0.
• --elevation_aug If set to 1, use elevation augmentation. Images will be rendered from a random elevation. Otherwise, set to 0.
• --resolution Image resolution. We set 256*256. If you want higher resolution, you can set 512 or 1024.
• --mode_multi If set to 1, use multi-view render mode. Images will be rendered from at 'time 0,view 0' to 'time T, view T'. Otherwise, set to 0.
• --mode_static If set to 1, use multi-static-view render mode. Images will be rendered from at 'time 0,view 0' to 'time 0, view T'. Otherwise, set to 0.
• --mode_front_view If set to 1, use front view render mode. Images will be rendered from at 'time 0,view front' to 'time T, view front'. The front view will change with azimuth augmention. Otherwise, set to 0.
• --mode_four_view If set to 1, use four view render mode. Images will be rendered from at 'time 0,view front,left,right,back' to 'time T, view front,left,right,back'. Otherwise, set to 0.

Output Explanation:

├── output
│   | object1
│     ├── multi_frame0-23.png          #mode_multi outputs 
│     ├── multi0-23.json               #mode_multi cameras 
│
│     ├── multi_static_frame0-23.png   #mode_static outputs
│     ├── static0-23.json              #mode_static cameras 
│
│     # optional
│     ├── front_frame0-23.png                   #mode_front_view outputs
│     ├── front.json                            #mode_front_view cameras
│     ├── front/left/right/back_frame0-23.png   #mode_four_view outputs
│     ├── front/left/right/back.json            #mode_four_view cameras
│
│   | object2
│   ....
│   | object3
│   ....

Our rendering script is based on point-e and Objaverse rendering scripts. Thanks a lot to all the authors for sharing!

Other codes will be released soon!

Acknowledgement

This project is based on numerous outstanding research efforts and open-source contributions. We are deeply grateful to all the authors for their generosity in sharing their work!

• https://github.com/alibaba/VideoMV
• https://github.com/hustvl/4DGaussians
• https://github.com/graphdeco-inria/gaussian-splatting
• https://github.com/graphdeco-inria/diff-gaussian-rasterization
• https://objaverse.allenai.org/objaverse-1.0/
• https://github.com/allenai/objaverse-xl
• https://github.com/crockwell/Cap3D
• https://github.com/openai/point-e

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}
}