4Diffusion: Multi-view Video Diffusion Model for 4D Generation

| Project Page | Paper |

Official code for 4Diffusion: Multi-view Video Diffusion Model for 4D Generation.

The paper presents a novel 4D generation pipeline, namely 4Diffusion, aimed at generating spatial-temporally consistent 4D content from a monocular video. We design a multi-view video diffusion model 4DM to capture multi-view spatial-temporal correlations for multi-view video generation.

Installation Requirements

The code is compatible with python 3.10.0 and pytorch 2.0.1. To create an anaconda environment named 4diffusion with the required dependencies, run:

conda create -n 4diffusion python==3.10.0
conda activate 4diffusion

pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

4D Data

We filter out animated 3D shapes from the vast 3D data corpus of Objaverse-1.0. We provide ids of the curated data in dataset/uid.npy. We will also release the rendered multi-view videos (To be uploaded) for future works.

Quickstart

Download pre-trained models

Please download 4DM and ImageDream modelcard and put them under ./ckpts/.

Multi-view Video Generation

To generate multi-view videos, run:

bash threestudio/models/imagedream/scripts/demo.sh

please configure the image(input monocular video path), text(text prompt), and num_video_frames(number of frames of input monocular video) in demo.sh. The results can be found in threestudio/models/imagedream/4dm.

We use rembg to segment the foreground object for 4D generation.

# name denotes the folder's name under threestudio/models/imagedream/4dm
python threestudio/models/imagedream/scripts/remove_bg.py --name yoda

4D Generation

To generate 4D content from a monocular video, run:

# system.prompt_processor_multi_view.prompt: text prompt
# system.prompt_processor_multi_view.image_path: monocular video path
# data.multi_view.image_path: anchor video path (anchor loss in Sec3.3)
# system.prompt_processor_multi_view.image_num: number of frames for training, default: 8
# system.prompt_processor_multi_view.total_num: number of frames of input monocular video
# data.multi_view.anchor_view_num: anchor view for anchor loss. 0: 0 azimuth; 1: 90 azimuth; 2: 180 azimuth; 3: 270 azimuth
python launch.py --config ./configs/4diffusion.yaml --train \ 
                system.prompt_processor_multi_view.prompt='baby yoda in the style of Mormookiee' \
                system.prompt_processor_multi_view.image_path='./threestudio/models/imagedream/assets/yoda/0_rgba.png' \
                data.multi_view.image_path='./threestudio/models/imagedream/4dm/yoda' \
                system.prompt_processor_multi_view.image_num=8 \
                system.prompt_processor_multi_view.total_num=25 \
                data.multi_view.anchor_view_num=0

The results can be found in outputs/4diffusion.

Citing

If you find 4Diffusion helpful, please consider citing:

@article{zhang20244diffusion,
  title={4Diffusion: Multi-view Video Diffusion Model for 4D Generation},
  author={Zhang, Haiyu and Chen, Xinyuan and Wang, Yaohui and Liu, Xihui and Wang, Yunhong and Qiao, Yu},
  journal={arXiv preprint arXiv:2405.20674},
  year={2024}
}

Credits

This code is built on the threestudio-project, 4D-fy, and ImageDream. Thanks to the maintainers for their contribution to the community!