Our enviroment is python3.8, pytorch1.13, cuda11.7, you can change the following instructions that suitable for your settings.

    sudo apt-get update -y
    sudo apt-get install libgl1
    sudo apt-get install libboost-dev

• pytorch3d code
• psbody-mesh code
• Kaolin code
• torch_geometric code

pip install torch_geometric
pip install pyg_lib-0.3.0+pt113cu117-cp38-cp38-linux_x86_64.whl
pip install torch_cluster-1.6.1+pt113cu117-cp38-cp38-linux_x86_64.whl
pip install torch_scatter-2.1.1+pt113cu117-cp38-cp38-linux_x86_64.whl
pip install torch_sparse-0.6.15+pt113cu117-cp38-cp38-linux_x86_64.whl

Cloth2Tex is composed of two phase: (1) Coarse texture generation and (2) Fine texture completion. Where Phase I is to determine the 3D garment shape and coarse texture. We do this by registering our parametric garment meshes onto catalog images using a neural mesh renderer. The pipeline’s Then Phase II is to recover fine textures from the coarse estimate of Phase I. We use image translation networks trained on large-scale data synthesized by pre-trained latent diffusion models.

We only made Phase I publicly available for now.

python phase1_inference.py --g 1_wy --s 1.2 --d "20231017_wy" --steps_one 501 --steps_two 1001

The optimized results are saved in experiments/20231017_wy, x_texture_uv_1000.jpg is the final UV texture.

Users can check it with Blender, remember you should only reserve one material, and remove other redundant materials for textured mesh.

The noteworthy thing here is that we are not make automatic scaling mechanism code publicly available, if you need it, you could self-implement it or manually adjust the --s (scale).

Default coefficient for test images:

per_scale_dict = {"1_wy": 1.1,
                  "2_Polo": 0.8, # default 0.8
                  "3_Tshirt": 0.9, # default 0.7
                  "4_shorts": 0.75, # # default 0.7
                  "5_trousers": 0.75,
                  "6_zipup": 1.1,
                  "7_windcoat": 0.65,
                  "9_jacket": 1.0,
                  "11_skirt": 1.0} 

We are not going to release the 2D landmark detector. If you need an accurate 2D landmarks in accordance with Cloth2Tex, you can annotate it manually or train a simple 2D cloth landmark detector with the same definition from Cloth2Tex.

We are applying for the open-source of Phase II, we will update once approval procedure has finished.

Please check cloth2tex web page for animated visual results: cloth2tex or check our youtube video youtube.

@article{gao2023cloth2tex,
  title={Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On},
  author={Gao, Daiheng and Chen, Xu and Zhang, Xindi and Wang, Qi and Sun, Ke and Zhang, Bang and Bo, Liefeng and Huang, Qixing},
  journal={arXiv preprint arXiv:2308.04288},
  year={2023}
}