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

Overview

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

report Open In Colab

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!!!
  • [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".

Project Page Teaser Image

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 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Warning: I found that outdated NVIDIA drivers may cause errors with EGL.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

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
  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

Related Research

Monocular Real-Time Volumetric Performance Capture (ECCV 2020)
Ruilong Li*, Yuliang Xiu*, Shunsuke Saito, Zeng Huang, Kyle Olszewski, Hao Li

The first real-time PIFu by accelerating reconstruction and rendering!!

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (CVPR 2020)
Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

We further improve the quality of reconstruction by leveraging multi-level approach!

ARCH: Animatable Reconstruction of Clothed Humans (CVPR 2020)
Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, Tony Tung

Learning PIFu in canonical space for animatable avatar generation!

Robust 3D Self-portraits in Seconds (CVPR 2020)
Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu

They extend PIFu to RGBD + introduce "PIFusion" utilizing PIFu reconstruction for non-rigid fusion.

Learning to Infer Implicit Surfaces without 3d Supervision (NeurIPS 2019)
Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li

We answer to the question of "how can we learn implicit function if we don't have 3D ground truth?"

SiCloPe: Silhouette-Based Clothed People (CVPR 2019, best paper finalist)
Ryota Natsume*, Shunsuke Saito*, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, Shigeo Morishima

Our first attempt to reconstruct 3D clothed human body with texture from a single image!

Deep Volumetric Video from Very Sparse Multi-view Performance Capture (ECCV 2018)
Zeng Huang, Tianye Li, Weikai Chen, Yajie Zhao, Jun Xing, Chloe LeGendre, Linjie Luo, Chongyang Ma, Hao Li

Implict surface learning for sparse view human performance capture!


For commercial queries, please contact:

Hao Li: [email protected] ccto: [email protected] Baker!!

PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
A GUI to automatically create a TOPAS-readable MLC simulation file

Python script to create a TOPAS-readable simulation file descriring a Multi-Leaf-Collimator. Builds the MLC using the data from a 3D .stl file.

Sebastian Schäfer 0 Jun 19, 2022
This repo is a PyTorch implementation for Paper "Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds"

Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds This repository is a PyTorch implementation for paper: Uns

Kaizhi Yang 42 Dec 09, 2022
基于PaddleOCR搭建的OCR server... 离线部署用

开头说明 DangoOCR 是基于大家的 CPU处理器 来运行的,CPU处理器 的好坏会直接影响其速度, 但不会影响识别的精度 ,目前此版本识别速度可能在 0.5-3秒之间,具体取决于大家机器的配置,可以的话尽量不要在运行时开其他太多东西。需要配合团子翻译器 Ver3.6 及其以上的版本才可以使用!

胖次团子 131 Dec 25, 2022
A minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose

Face-Detection-flask-gunicorn-nginx-docker This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and

Pooya-Mohammadi 30 Dec 17, 2022
The dynamics of representation learning in shallow, non-linear autoencoders

The dynamics of representation learning in shallow, non-linear autoencoders The package is written in python and uses the pytorch implementation to ML

Maria Refinetti 4 Jun 08, 2022
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Benedek Rozemberczki 490 Dec 16, 2022
Google Landmark Recogntion and Retrieval 2021 Solutions

Google Landmark Recogntion and Retrieval 2021 Solutions In this repository you can find solution and code for Google Landmark Recognition 2021 and Goo

Vadim Timakin 5 Nov 25, 2022
SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Brown University Scale Lab 4 Nov 18, 2022
Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

FIB LAB, Tsinghua University 53 Dec 26, 2022
Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

37 Dec 03, 2022
[SIGGRAPH 2021 Asia] DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

DeepVecFont This is the official Pytorch implementation of the paper: Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts

Yizhi Wang 146 Dec 18, 2022
Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources.

Illumination_Decomposition Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources. This code implements the

QAY 7 Nov 15, 2020
MEND: Model Editing Networks using Gradient Decomposition

MEND: Model Editing Networks using Gradient Decomposition Setup Environment This codebase uses Python 3.7.9. Other versions may work as well. Create a

Eric Mitchell 141 Dec 02, 2022
3rd Place Solution of the Traffic4Cast Core Challenge @ NeurIPS 2021

3rd Place Solution of Traffic4Cast 2021 Core Challenge This is the code for our solution to the NeurIPS 2021 Traffic4Cast Core Challenge. Paper Our so

7 Jul 25, 2022
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations

Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations This repo contains official code for the NeurIPS 2021 paper Imi

Jiayao Zhang 2 Oct 18, 2021
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

GemNet: Universal Directional Graph Neural Networks for Molecules Reference implementation in PyTorch of the geometric message passing neural network

Data Analytics and Machine Learning Group 124 Dec 30, 2022
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for

Guosheng Lin 575 Dec 06, 2022
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [Official Paddle Implementation] [Huggingface Gradio Demo] [Unofficial

442 Dec 16, 2022