Zerong Zheng, Tao Yu, Yebin Liu, Qionghai Dai
This repository contains a pytorch implementation of "PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction". Tested with PyTorch 1.7.0 on Ubuntu 18.04, CUDA 11.0.
NOTE: The current models are trained on private Twindom scans, which are bias towards upright standing poses. Fortunately, we have collected THUman 2.0, a new dataset containing 500 high-quality 3D human scans with more variety in body poses. You can use THUman 2.0 to train/fine-tune our PaMIR model or your own models!
NOTE 2021/11/16: The pre-trained model I previously uploaded is not the correct one. I have replaced it with the correct model. Sincere apology!
If you find the code useful in your research, please consider citing the paper.
@misc{zheng2020pamir,
title={PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction},
author={Zerong Zheng, Tao Yu, Yebin Liu, Qionghai Dai},
journal={IEEE Transactions on Pattern Analysis and Machine Intelegence},
year={2021},
primaryClass={cs.CV}
}
Please run the following commands to download necessary assets (including the pre-trained models):
cd ./networks
wget https://github.com/ZhengZerong/PaMIR/releases/download/v0.0/results.zip
unzip -o results.zip
cd ..After that, run the following script to test the pre-trained network:
cd ./networks
python main_test.py
cd ..This command will generate the textured reconstruction with the fitted SMPLs for the example input images in ./network/results/test_data*/. Note that we assume the input images are tightly cropped with the background removed and the height of the persons is about 80% of the image height (Please see the example input images we provide).
In dataset_example, we provide an example data item, which contains a textured mesh and a SMPL model fitted to the mesh. The mesh is downloaded from RenderPeople. To generate the training images, please run:
cd ./data
python main_normalize_mesh.py # we normalize all scans into a unit bounding box
python main_calc_prt.py
python main_render_images.py
python main_sample_occ.py
python main_associate_points_with_smpl_vertices.py # requires SMPL fitting
cd ..Note that the last python script requires SMPL model fitted to the scans. To fit SMPL to your own 3D scans, you can use our tool released at this link.
Please run the following command to train the network:
cd ./networks
bash ./scripts/train_script_geo.sh # geometry network
bash ./scripts/train_script_tex.sh # texture network
cd ..Note that the rendering code of this repo is heavily based on PIFU, while the training code is heavily based on GraphCMR. We thank the authors for their great job!
Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use PaMIR Software/Code/Data (the "Software"). By downloading and/or using the Software, you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Software.
Ownership
The Software has been developed at the Tsinghua University and is owned by and proprietary material of the Tsinghua University.
License Grant
Tsinghua University grants you a non-exclusive, non-transferable, free of charge right:
To download the Software and use it on computers owned, leased or otherwise controlled by you and/or your organisation;
To use the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.
Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, as training data for a commercial product, for commercial ergonomic analysis (e.g. product design, architectural design, etc.), or production of other artifacts for commercial purposes including, for example, web services, movies, television programs, mobile applications, or video games. The Software may not be used for pornographic purposes or to generate pornographic material whether commercial or not. This license also prohibits the use of the Software to train methods/algorithms/neural networks/etc. for commercial use of any kind. The Software may not be reproduced, modified and/or made available in any form to any third party without Tsinghua University’s prior written permission. By downloading the Software, you agree not to reverse engineer it.
Disclaimer of Representations and Warranties
You expressly acknowledge and agree that the Software results from basic research, is provided “AS IS”, may contain errors, and that any use of the Software is at your sole risk. TSINGHUA UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE SOFTWARE, NEITHER EXPRESS NOR IMPLIED, AND THE ABSENCE OF ANY LEGAL OR ACTUAL DEFECTS, WHETHER DISCOVERABLE OR NOT. Specifically, and not to limit the foregoing, Tsinghua University makes no representations or warranties (i) regarding the merchantability or fitness for a particular purpose of the Software, (ii) that the use of the Software will not infringe any patents, copyrights or other intellectual property rights of a third party, and (iii) that the use of the Software will not cause any damage of any kind to you or a third party.
Limitation of Liability
Under no circumstances shall Tsinghua University be liable for any incidental, special, indirect or consequential damages arising out of or relating to this license, including but not limited to, any lost profits, business interruption, loss of programs or other data, or all other commercial damages or losses, even if advised of the possibility thereof.
No Maintenance Services
You understand and agree that Tsinghua University is under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. Tsinghua University nevertheless reserves the right to update, modify, or discontinue the Software at any time.
Publication with the Software
You agree to cite the paper describing the software and algorithm as specified on the download website.
Media Projects with the Software
When using the Software in a media project please give credit to Tsinghua University. For example: the Software was used for performance capture courtesy of the Tsinghua University.
Commercial Licensing Opportunities
For commercial use and commercial license please contact: [email protected].
- Zerong Zheng ([email protected])
- Yebin Liu ([email protected])