Awesome
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
What is new?
-
RSC-Net:
- Resolution-aware structure
- Self-supervised learning
- Contrastive learning
-
Temporal post-processing for video input
-
TexGlo: Global module for 3D texture reconstruction
Brief introduction
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
- Download pretrained RSC-Net, and put it in "./pretrained".
- Run:
python demo.py --checkpoint=./pretrained/RSC-Net.pt --img_path=./examples/im1.png
- Note: if you have trouble in using Pyrender, please try "demo_nr.py":
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},
}