This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

If you find the code useful in your research, please consider citing the paper.

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


  • Python 3
  • PyTorch tested on 1.4.0
  • json
  • PIL
  • skimage
  • tqdm
  • numpy
  • cv2

for training and data generation

  • trimesh with pyembree
  • 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Warning: I found that outdated NVIDIA drivers may cause errors with EGL.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
  • Add conda to PATH
  • Install git bash
  • Launch Git\bin\bash.exe
  • eval "$(conda shell.bash hook)" then conda activate my_env because of this
  • Automatic env create -f environment.yml (look this)
  • OR manually setup environment
    • 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
  • Place it into Git\mingw64\bin
  • sh ./scripts/download_trained_model.sh
  • Remove background from your image (this, for example)
  • Create black-white mask .png
  • Replace original from sample_images/
  • Try it out - sh ./scripts/test.sh
  • Download Meshlab because of this
  • Open .obj file in Meshlab


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
  1. run the following script. the script creates a textured .obj file under ./PIFu/eval_results/. You may need to use ./apps/crop_img.py to roughly align an input image and the corresponding mask to the training data for better performance. For background removal, you can use any off-the-shelf tools such as removebg.
sh ./scripts/test.sh

Demo on Google Colab

If you do not have a setup to run PIFu, we offer Google Colab version to give it a try, allowing you to run PIFu in the cloud, free of charge. Try our Colab demo using the following notebook:
Open In Colab

Data Generation (Linux Only)

While we are unable to release the full training data due to the restriction of commertial scans, we provide rendering code using free models in RenderPeople.
This tutorial uses rp_dennis_posed_004 model. Please download the model from this link and unzip the content under a folder named rp_dennis_posed_004_OBJ. The same process can be applied to other RenderPeople data.

Warning: the following code becomes extremely slow without pyembree. Please make sure you install pyembree.

  1. run the following script to compute spherical harmonics coefficients for precomputed radiance transfer (PRT). In a nutshell, PRT is used to account for accurate light transport including ambient occlusion without compromising online rendering time, which significantly improves the photorealism compared with a common sperical harmonics rendering using surface normals. This process has to be done once for each obj file.
python -m apps.prt_util -i {path_to_rp_dennis_posed_004_OBJ}
  1. run the following script. Under the specified data path, the code creates folders named GEO, RENDER, MASK, PARAM, UV_RENDER, UV_MASK, UV_NORMAL, and UV_POS. Note that you may need to list validation subjects to exclude from training in {path_to_training_data}/val.txt (this tutorial has only one subject and leave it empty). If you wish to render images with headless servers equipped with NVIDIA GPU, add -e to enable EGL rendering.
python -m apps.render_data -i {path_to_rp_dennis_posed_004_OBJ} -o {path_to_training_data} [-e]

Training (Linux Only)

Warning: the following code becomes extremely slow without pyembree. Please make sure you install pyembree.

  1. run the following script to train the shape module. The intermediate results and checkpoints are saved under ./results and ./checkpoints respectively. You can add --batch_size and --num_sample_input flags to adjust the batch size and the number of sampled points based on available GPU memory.
python -m apps.train_shape --dataroot {path_to_training_data} --random_flip --random_scale --random_trans
  1. run the following script to train the color module.
python -m apps.train_color --dataroot {path_to_training_data} --num_sample_inout 0 --num_sample_color 5000 --sigma 0.1 --random_flip --random_scale --random_trans