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4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

Authors: Yuyang Yin, Dejia Xu, Zhangyang Wang, Yao Zhao, Yunchao Wei

[Project Page] | [Video (narrated)] | [Video (results only)] | [Paper] | [Arxiv]

overview

News

2024/5/26 Release our new work. Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models. You can click project page to learn more detail.
2023/12/28 First release code and paper.
2024/2/14 Update text-to-4d and image-to-4d functions and cases.
2024/3/17 Add a completed example script.

Task Type

As show in figure above, we define grounded 4D generation, which focuses on video-to-4D generation. Video is not required to be user-specified but can also be generated by video diffusion. With the help of stable video diffusion, we implement the function of image-to-video-to-4d and text-to-image-to-video-to-4d . Due to the unsatisfactory performance of the text-to-video model, we use stable diffusion-XL and stable video diffusion implement the function of text-to-image-to-video-to-4d. Therefore, our model support text-to-4D and image-to-4D tasks.

Setup

conda env create -f environment.yml
conda activate 4DGen
pip install -r requirements.txt

# 3D Gaussian Splatting modules, skip if you already installed them
# a modified gaussian splatting (+ depth, alpha rendering)
git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
pip install ./diff-gaussian-rasterization
pip install ./simple-knn

# install kaolin for chamfer distance (optional)
# https://kaolin.readthedocs.io/en/latest/notes/installation.html
# CHANGE the torch and CUDA toolkit version if yours are different
# pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.1_cu116.html

Example Case Script

We have organized a complete pipeline script in main.bash for your reference. You need to modify the necessary paths.

Data Preparation

We release our collected data in Google Drive. Some of these data are user-specified, while others are generated.

Each test case contains two folders: {name}_pose0 and {name}_sync. pose0 refers to the monocular video sequence. sync refers to the pseudo labels generated by SyncDreamer.

We recommend using Practical-RIFE if you need to introduce more frames in your video sequence.

Text-To-4D data prepartion

Use stable diffusion-XL to generate your own images. Then use image-to-video script below.

Image-To-4D data prepartion

python image_to_video.py --data_path {your image.png} --name {file name}  #It may be necessary to try multiple seeds to obtain the desired results.

Preprocess data format for training

To preprocess your own images into RGBA format, you can use preprocess.py .

To preprocess your own images to multi view images, you can use SyncDreamer script，then use preprocess_sync.py to get a uniform format.

# for monocular image sequence
python preprocess.py --path xxx
# for images generated by syncdreamer
python preprocess_sync.py --path xxx

Training

python train.py --configs arguments/i2v.py -e rose --name_override rose

Rendering

python render.py --skip_train --configs arguments/i2v.py --skip_test --model_path "./output/xxxx/"

Evaluation

Please see main.bash.

Result

We show part of results in our web pages.

Image-to-4D results:

frontview_mario	multiview_mario

Text-to-4D results:

We first use stable-diffusion-xl to generate a static image. Prompt is 'an emoji of a baby panda, 3d model, front view'.

frontview_panda	multiview-panda

Acknowledgement

This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!

Citation

If you find this repository/work helpful in your research, please consider citing the paper and starring the repo ⭐.

@article{yin20234dgen,
  title={4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency},
  author={Yin, Yuyang and Xu, Dejia and Wang, Zhangyang and Zhao, Yao and Wei, Yunchao},
  journal={arXiv preprint arXiv:2312.17225},
  year={2023}
}}