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This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"
| Date | Stars |
|---|---|
| 2026-07-24 | 1819 |
| 2026-07-25 | 1818 |
| 2026-07-28 | 1818 |
| 2026-07-30 | 1818 |
| 2026-08-06 | 1818 |
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# PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
[](https://arxiv.org/abs/1905.05172) [](https://colab.research.google.com/drive/1GFSsqP2BWz4gtq0e-nki00ZHSirXwFyY)
News:
* \[2020/05/04\] Added EGL rendering option for training data generation. Now you can create your own training data with headless machines!
* \[2020/04/13\] Demo with Google Colab (incl. visualization) is available. Special thanks to [@nanopoteto](https://github.com/nanopoteto)!!!
* \[2020/02/26\] License is updated to MIT license! Enjoy!
This repository contains a pytorch implementation of "[PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://arxiv.org/abs/1905.05172)".
[Project Page](https://shunsukesaito.github.io/PIFu/)
If you find the code useful in your research, please consider citing the paper.
```
@InProceedings{saito2019pifu,
author = {Saito, Shunsuke and Huang, Zeng and Natsume, Ryota and Morishima, Shigeo and Kanazawa, Angjoo and Li, Hao},
title = {PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```
This codebase provides:
- test code
- training code
- data generation code
## Requirements
- Python 3
- [PyTorch](https://pytorch.org/) tested on 1.4.0
- json
- PIL
- skimage
- tqdm
- numpy
- cv2
for training and data generation
- [trimesh](https://trimsh.org/) with [pyembree](https://github.com/scopatz/pyembree)
- [pyexr](https://github.com/tvogels/pyexr)
- PyOpenGL
- freeglut (use `sudo apt-get install freeglut3-dev` for ubuntu users)
- (optional) egl related packages for rendering with headless machines. (use `apt install libgl1-mesa-dri libegl1-mesa libgbm1` for ubuntu users)
Warning: I found that outdated NVIDIA drivers may cause errors with EGL. If you want to try out the EGL version, please update your NVIDIA driver to the latest!!
## Windows demo installation instuction
- Install [miniconda](https://docs.conda.io/en/latest/miniconda.html)
- Add `conda` to PATH
- Install [git bash](https://git-scm.com/downloads)
- Launch `Git\bin\bash.exe`
- `eval "$(conda shell.bash hook)"` then `conda activate my_env` because of [this](https://github.com/conda/conda-build/issues/3371)
- Automatic `env create -f environment.yml` (look [this](https://github.com/conda/conda/issues/3417))
- OR manually setup [environment](https://towardsdatascience.com/a-guide-to-conda-environments-bc6180fc533)
- `conda create —name pifu python` where `pifu` is name of your environment
- `conda activate`
- `conda install pytorch torchvision cudatoolkit=10.1 -c pytorch`
- `conda install pillow`
- `conda install scikit-image`
- `conda install tqdm`
- `conda install -c menpo opencv`
- Download [wget.exe](https://eternallybored.org/misc/wget/)
- Place it into `Git\mingw64\bin`
- `sh ./scripts/download_trained_model.sh`
- Remove background from your image ([this](https://www.remove.bg/), for example)
- Create black-white mask .png
- Replace original from sample_images/
- Try it out - `sh ./scripts/test.sh`
- Download [Meshlab](http://www.meshlab.net/) because of [this](https://github.com/shunsukesaito/PIFu/issues/1)
- Open .obj file in Meshlab
## Demo
Warning: The released model is trained with mostly upright standing scans with weak perspectie projection and the pitch angle of 0 degree. Reconstruction quality may degrade for images highly deviated from trainining data.
1. run the following script to download the pretrained models from the following link and copy them under `./PIFu/checkpoints/`.
```
sh ./scripts/download_trained_model.sh
```
2. run the following script. the script creates a textured `.obj` fiExcerpt of 9,552 characters
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Christian Clauss · Christian Clauss · Switzerland
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f32927d57b2b343f, topic:3d-reconstruction, readme:computer vision
matched fp:f32927d57b2b343f, topic:pytorch