HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation

Project Page | Paper | Video

Official implementation of HumanNorm, a method for generating high-quality and realistic 3D Humans from prompts.

Xin Huang¹, Ruizhi Shao², Qi Zhang¹, Hongwen Zhang², Ying Feng¹, Yebin Liu², Qing Wang¹
¹Northwestern Polytechnical University, ²Tsinghua University, ^*Equal Contribution

CVPR 2024

https://github.com/xhuangcv/humannorm/assets/28997098/892cbbfa-05d3-4481-b7f5-fcae739ac8c9

Method Overview

Installation

This part is the same as the original threestudio. Skip it if you already have installed the environment.

See installation.md for additional information, including installation via Docker.

You must have an NVIDIA graphics card with at least 20GB VRAM and have CUDA installed.
Install Python >= 3.8.
(Optional, Recommended) Create a virtual environment:

pip3 install virtualenv # if virtualenv is installed, skip it
python3 -m virtualenv venv
. venv/bin/activate

# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.
# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.
python3 -m pip install --upgrade pip

Install PyTorch >= 1.12. We have tested on torch1.12.1+cu113 and torch2.0.0+cu118, but other versions should also work fine.

# torch1.12.1+cu113
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# or torch2.0.0+cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

(Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:

pip install ninja

Install dependencies:

pip install -r requirements.txt

(Optional) tiny-cuda-nn installation might require downgrading pip to 23.0.1

Download Finetuned Models

You can download our fine-tuned models on HuggingFace: Normal-adapted-model, Depth-adapted-model, Normal-aligned-model and ControlNet. We provide the script to download load these models.

./download_models.sh

After downloading, the pretrained_models/ is structured like:

./pretrained_models
├── normal-adapted-sd1.5/
├── depth-adapted-sd1.5/
├── normal-aligned-sd1.5/
└── controlnet-normal-sd1.5/

Download Tets

You can download the predefined Tetrahedra for DMTET by

sudo apt-get install git-lfs # install git-lfs
cd load/
sudo chmod +x download.sh
./download.sh

After downloading, the load/ is structured like:

./load
├── lights/
├── shapes/
└── tets
    ├── ...
    ├── 128_tets.npz
    ├── 256_tets.npz
    ├── 512_tets.npz
    └── ...

Quickstart

The directory scripts contains scripts used for full-body, half-body, and head-only human generations. The directory configs contains parameter settings for all these generations. HumanNorm generates 3D humans in three steps including geometry generation, coarse texture generation, and fine texture generation. You can directly execute these three steps using these scripts. For example,

./script/run_generation_full_body.sh

After generation, you can get the result for each step.

https://github.com/xhuangcv/humannorm/assets/28997098/c728fc44-a205-4349-a259-88f121709318

You can also modify the prompt in run_generation_full_body.sh to generate other models. The script looks like this:

#!/bin/bash
exp_root_dir="./outputs"
test_save_path="./outputs/rgb_cache"
timestamp="_20231223"
tag="curry"
prompt="a DSLR photo of Stephen Curry"

# Stage1: geometry generation
exp_name="stage1-geometry"
python launch.py \
    --config configs/humannorm-geometry-full.yaml \
    --train \
    timestamp=$timestamp \
    tag=$tag \
    name=$exp_name \
    exp_root_dir=$exp_root_dir \
    data.sampling_type="full_body" \
    system.prompt_processor.prompt="$prompt, black background, normal map" \
    system.prompt_processor_add.prompt="$prompt, black background, depth map" \
    system.prompt_processor.human_part_prompt=false \
    system.geometry.shape_init="mesh:./load/shapes/full_body.obj"

# Stage2: coarse texture generation
geometry_convert_from="$exp_root_dir/$exp_name/$tag$timestamp/ckpts/last.ckpt" 
exp_name="stage2-coarse-texture"
root_path="./outputs/$exp_name"
python launch.py \
    --config configs/humannorm-texture-coarse.yaml \
    --train \
    timestamp=$timestamp \
    tag=$tag \
    name=$exp_name \
    exp_root_dir=$exp_root_dir \
    system.geometry_convert_from=$geometry_convert_from \
    data.sampling_type="full_body" \
    data.test_save_path=$test_save_path \
    system.prompt_processor.prompt="$prompt" \
    system.prompt_processor.human_part_prompt=false

# Stage3: fine texture generation
ckpt_name="last.ckpt"
exp_name="stage3-fine-texture"
python launch.py \
    --config configs/humannorm-texture-fine.yaml \
    --train \
    system.geometry_convert_from=$geometry_convert_from \
    data.dataroot=$test_save_path \
    timestamp=$timestamp \
    tag=$tag \
    name=$exp_name \
    exp_root_dir=$exp_root_dir \
    resume="$root_path/$tag$timestamp/ckpts/$ckpt_name" \
    system.prompt_processor.prompt="$prompt" \
    system.prompt_processor.human_part_prompt=false

Todo

[x] Release the reorganized code.
[ ] Improve the quality of texture generation.
[ ] Release the finetuning code.

Citation

If you find our work useful in your research, please cite:

@article{huang2023humannorm,
  title={Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation},
  author={Huang, Xin and Shao, Ruizhi and Zhang, Qi and Zhang, Hongwen and Feng, Ying and Liu, Yebin and Wang, Qing},
  journal={arXiv preprint arXiv:2310.01406},
  year={2023}
}

Acknowledgments

Our project benefits from the amazing open-source projects:

We are grateful for their contribution.

xhuangcv / humannorm

readme