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[ICCV 2025] This repo is an official PyTorch implementation of PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image.

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# PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image

[Hyeongjin Nam](https://hygenie1228.github.io/)*1, [Donghwan Kim](https://kimdonghwan06.github.io/)*1, [Gyeongsik Moon](https://mks0601.github.io/)&dagger;2, [Kyoung Mu Lee](https://cv.snu.ac.kr/index.php/~kmlee/)1

1Seoul National University, 2Korea University
(*Equal contribution)

<a>![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-brightgreen.svg)</a>
<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
<a href='https://hygenie1228.github.io/PARTE/'><img src='https://img.shields.io/badge/Project_Page-PARTE-green' alt='Project Page'></a>
<a href="https://hygenie1228.github.io/PARTE/static/PARTE_ICCV2025.pdf"><img src='https://img.shields.io/badge/Paper-PARTE-blue' alt='Paper PDF'></a>
<a href="https://arxiv.org/abs/2507.17332"><img src='https://img.shields.io/badge/arXiv-PARTE-red' alt='Paper PDF'></a>

</div>

_**PARTE** is a 3D human reconstruction framework that recovers realistsic human textures that is well-aligned to each human parts. This framework estimates **3D part segmentations** of the human surface and utilizes them as main guidance for reconstructing human textures._

## Installation
We recommend you to use an [Anaconda](https://www.anaconda.com/) virtual environment. Our latest PARTE model is tested on Python 3.10.16, PyTorch 2.1.2, CUDA 12.1. 
Setup the environment with the script below.
``` 
# Initialize conda environment
conda create -n parte python=3.10
conda activate parte

# Install PyTorch
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121

# Install remaining packages
bash scripts/install.sh

# Install thirdparty modules
bash scripts/clone_thirdparties.sh
```

## Data
Please follow the [Download Instruction](scripts/download.md) to download all the required data files.

## Checkpoints
Download our released checkpoints of SegmentNet and PartDiffusion through our [Huggingface repo](https://huggingface.co/kdh1106/PARTE_weights) under `${ROOT}/data/checkpoints/`.

## Running PARTE
* To run PARTE with your image, please prepare an `yaml` file that contains text prompts for each body parts as in `${ROOT}/configs/prompts.yaml`, and run the following code.
```
    cd main
    python demo.py --img_path {PATH/TO/YOUR_IMAGE} --prompt_path {PATH/TO/YOUR_PROMPT} --exp_dir {PATH/TO/SAVE_DIR}
```

* (Optional) You can also automatically generate text prompts using [GPT-4o](https://platform.openai.com/docs/models/gpt-4o) with the `--run_vllm` option. Please place your [OpenAI](https://platform.openai.com/api-keys) API key to `${ROOT}/data/openai.env` and run the following code.
```
    cd main
    python demo.py --img_path {PATH/TO/YOUR_IMAGE} --exp_dir {PATH/TO/SAVE_DIR} --run_vllm
```

## Acknowledgement
We thank:
* [TeCH](https://github.com/huangyangyi/TeCH) for 3D human mesh reconsturction.
* [Sapiens](https://github.com/facebookresearch/sapiens) for human image segmentation.
* [InstanceDiffusion](https://github.com/frank-xwang/InstanceDiffusion) for PartDiffusion training setup.

## Reference
```  
@inproceedings{nam2025parte,    
title = {PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image},
author = {Nam, Hyeongjin and Kim, Donghwan and Moon, Gyeongsik and Lee, Kyoung Mu},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},  
year = {2025}  
}  
```

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