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We present Tex4D, a zero-shot approach that integrates inherent 3D geometry knowledge from mesh sequences with the expressiveness of video diffusion models to produce multi-view and temporally consistent 4D textures. Given an untextured mesh sequence and a text prompt as inputs, our method generates multi-view, temporally consistent 4D textures.

• Technical Report
• Release inference code
• Release data preprocess code

Installation

Please first run following commands to build dependencies:

git clone https://github.com/ZqlwMatt/Tex4D.git
cd Tex4D
conda create -n tex4d python=3.8
conda activate tex4d
pip install -r requirements.txt

Then install PyTorch3D through the following URL (check and replace your CUDA verison by running pytorch3d_install.py)

pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu117_pyt200/download.html

Quick Start

1. Preprocess

Generate the conditioning data for the video diffusion model based on the provided mesh sequences.

python visualize.py --render --data_folder "anim/boo" --pose_dir "pose_3" --load_from_data

2. Run

python run.py --config data/boo/config.yaml

Sample results

For more see our project webpage.

https://github.com/user-attachments/assets/1ec1cc6e-34b5-4cb6-a175-1894bf69360f

https://github.com/user-attachments/assets/3b80a0ed-b201-469d-8cc8-137e82abefa2

https://github.com/user-attachments/assets/e4ca6c1b-3a3b-4da1-9bb8-9c217d48e100

Citation

@article{bao2024tex4d,
    title={Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models}, 
    author={Jingzhi Bao and Xueting Li and Ming-Hsuan Yang},
    journal={arXiv preprint arxiv:2410.10821},
    year={2024}
}