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Human 3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models

Project Page | Paper

NeurIPS, 2024

Yuxuan Xue1 , Xianghui Xie1, 2, Riccardo Marin1, Gerard Pons-Moll1, 2

1Real Virtual Human Group @ University of Tübingen & Tübingen AI Center
2Max Planck Institute for Informatics, Saarland Informatics Campus

News 🚩

  • [2024/10/07] Inference Code release.
  • [2024/09/25] Human 3Diffusion is accepted to NeurIPS 2024.
  • [2024/06/14] Human 3Diffusion paper is available on ArXiv.
  • [2024/06/14] Inference code and model weights is scheduled to be released after CVPR 2024.

Key Insight 🙌

  • 2D foundation models are powerful but output lacks 3D consistency!
  • 3D generative models can reconstruct 3D representation but is poor in generalization!
  • How to combine 2D foundation models with 3D generative models?:
    • they are both diffusion-based generative models => Can be synchronized at each diffusion step
    • 2D foundation model helps 3D generation => provides strong prior informations about 3D shape
    • 3D representation guides 2D diffusion sampling => use rendered output from 3D reconstruction for reverse sampling, where 3D consistency is guaranteed

Install

# Conda environment
conda create -n human3diffusion python=3.10
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
pip install xformers==0.0.22.post4 --index-url https://download.pytorch.org/whl/cu121

# Gaussian Opacity Fields
git clone https://github.com/YuxuanSnow/gaussian-opacity-fields.git
cd gaussian-opacity-fields && pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn/ && cd ..
export CPATH=/usr/local/cuda-12.1/targets/x86_64-linux/include:$CPATH

# Dependencies
pip install -r requirements.txt

# TSDF Fusion (Mesh extraction) Dependencies
pip install --user numpy opencv-python scikit-image numba
pip install --user pycuda
pip install scipy==1.11

Pretrained Weights

Our pretrained weight can be downloaded from huggingface.

mkdir checkpoints && cd checkpoints
wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/model.safetensors
wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/model_1.safetensors
wget https://huggingface.co/yuxuanx/human3diffusion/resolve/main/pifuhd.pt
cd ..

Inference

# given one image, generate 3D-GS
# subject should be centered in a square image, please crop properly
python infer.py --test_imgs test_imgs --output output --checkpoints checkpoints

# given generated 3D-GS, perform TSDF mesh extraction
python infer_mesh.py --test_imgs test_imgs --output output --checkpoints checkpoints --mesh_quality high

Citation ✍️

@inproceedings{xue2023human3diffusion,
  title     = {{Human 3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models}},
  author    = {Xue, Yuxuan and Xie, Xianghui and Marin, Riccardo and Pons-Moll, Gerard.},
  journal   = {NeurIPS 2024},
  year      = {2024},
}