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RSC-Net: 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

Implementation for "3D Human Pose, Shape and Texture from Low-Resolution Images and Videos", TPAMI 2021

Conference version: "3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning", ECCV 2020

Project page

What is new?

Brief introduction

Alt Text

Video

Video

Code

Packages

Make sure you have gcc==5.x.x for installing the packages. Then run:

bash install_environment.sh

If you are running the code without a screen, please install OSMesa and the corresponding PyOpenGL. Then uncomment the 2nd line of "utils/renderer.py".

Data preparation

Note that all paths are set in "config.py".

Demo

python demo.py --checkpoint=./pretrained/RSC-Net.pt --img_path=./examples/im1.png
python demo_nr.py --checkpoint=./pretrained/RSC-Net.pt --img_path=./examples/im1.png

If your neural-renderer has errors, please re-install the package from the source.

Evaluation

python eval.py --checkpoint=./pretrained/RSC-Net.pt 

Training

python train.py --name=RSC-Net 

   

If you find this work helpful in your research, please cite our paper:

@article{xu20213d,
title={3D Human Pose, Shape and Texture from Low-Resolution Images and Videos},
author={Xu, Xiangyu and Chen, Hao and Moreno-Noguer, Francesc and Jeni, Laszlo A and De la Torre, Fernando},
journal={TPAMI},
year={2021},
}

@inproceedings{xu20203d,
title={3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning},
author={Xu, Xiangyu and Chen, Hao and Moreno-Noguer, Francesc and Jeni, Laszlo A and De la Torre, Fernando},
booktitle={ECCV},
year={2020},
}