News:
This repository contains a pytorch implementation of "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.
@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:
for training and data generation
sudo apt-get install freeglut3-dev
for ubuntu users)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for ubuntu users)Warning: I found that outdated NVIDIA drivers may cause errors with EGL.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!!
conda
to PATHGit\bin\bash.exe
eval "$(conda shell.bash hook)"
then conda activate my_env
because of this
env create -f environment.yml
(look this)conda create —name pifu python
where pifu
is name of your environmentconda 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
Git\mingw64\bin
sh ./scripts/download_trained_model.sh
sh ./scripts/test.sh
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.
./PIFu/checkpoints/
.sh ./scripts/download_trained_model.sh
.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
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:
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.
python -m apps.prt_util -i {path_to_rp_dennis_posed_004_OBJ}
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]
Warning: the following code becomes extremely slow without pyembree. Please make sure you install pyembree.
./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
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
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: hao@hao-li.com ccto: saitos@usc.edu Baker!!
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