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Warning: I found that outdated NVIDIA drivers may cause errors with EGL.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

LIAO Shuiying 6 Dec 01, 2022
Self-Supervised Multi-Frame Monocular Scene Flow (CVPR 2021)

Self-Supervised Multi-Frame Monocular Scene Flow 3D visualization of estimated depth and scene flow (overlayed with input image) from temporally conse

Visual Inference Lab @TU Darmstadt 85 Dec 22, 2022
Official code for "Stereo Waterdrop Removal with Row-wise Dilated Attention (IROS2021)"

Stereo-Waterdrop-Removal-with-Row-wise-Dilated-Attention This repository includes official codes for "Stereo Waterdrop Removal with Row-wise Dilated A

29 Oct 01, 2022
Steerable discovery of neural audio effects

Steerable discovery of neural audio effects Christian J. Steinmetz and Joshua D. Reiss Abstract Applications of deep learning for audio effects often

Christian J. Steinmetz 182 Dec 29, 2022
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
An open source object detection toolbox based on PyTorch

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

Bo Chen 24 Dec 28, 2022
This is an official implementation for "ResT: An Efficient Transformer for Visual Recognition".

ResT By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the official implement

zhql 222 Dec 13, 2022
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

yobi byte 29 Oct 09, 2022
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well i

0 Sep 06, 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
EEGEyeNet is benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty

Introduction EEGEyeNet EEGEyeNet is a benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty. Overview T

Ard Kastrati 23 Dec 22, 2022
This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

[CVPRW 2021] - Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Anirudh S Chakravarthy 6 May 03, 2022
public repo for ESTER dataset and modeling (EMNLP'21)

Project / Paper Introduction This is the project repo for our EMNLP'21 paper: https://arxiv.org/abs/2104.08350 Here, we provide brief descriptions of

PlusLab 19 Oct 27, 2022
Paper Title: Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution

HKDnet Paper Title: "Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution" Email:

wasteland 11 Nov 12, 2022
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
[NeurIPS 2021] "Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems"

Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems Introduction Multi-agent control i

VITA 6 May 05, 2022
Interactive dimensionality reduction for large datasets

BlosSOM 🌼 BlosSOM is a graphical environment for running semi-supervised dimensionality reduction with EmbedSOM. You can use it to explore multidimen

19 Dec 14, 2022
A developer interface for creating Chat AIs for the Chai app.

ChaiPy A developer interface for creating Chat AIs for the Chai app. Usage Local development A quick start guide is available here, with a minimal exa

Chai 28 Dec 28, 2022
A curated (most recent) list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Jiaheng Wei 321 Jan 09, 2023
Hyper-parameter optimization for sklearn

hyperopt-sklearn Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. See how to use hyperopt-sklearn

1.4k Jan 01, 2023