# CVPR2020-OOH

**Repository Path**: seuvcl/CVPR2020-OOH

## Basic Information

- **Project Name**: CVPR2020-OOH
- **Description**: This is the repository of the implemantation of cvpr 2020. 
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 28
- **Forks**: 6
- **Created**: 2020-05-16
- **Last Updated**: 2024-07-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Object-Occluded Human Shape and Pose Estimation from a Single Color Image (CVPR2020 Oral)

## Introduction
This is the offical implentation of 'Object-Occluded Human Shape and Pose Estimation from a Single Color Image' (CVPR2020 Oral/TPAMI2022). [[Project Page](https://www.yangangwang.com/papers/ZHANG-OOH-2020-03.html)]

![figure](https://images.gitee.com/uploads/images/2020/0601/202625_9aa5f92b_5505182.png "pipeline1.png")

## News!
- Feb 2023: We released the video version of 3DOH50K dataset [link](https://github.com/boycehbz/CHOMP#ocmotion-dataset).
- Mar 2022: We add mesh fitting codes which fit SMPL model to the regressed mesh.
- Aug 2021: New model with ResNet-50 backbone. A heatmap branch is added to overlay regressed meshes to RGB images.

## Environment
This code is based on Python 3.6, CUDA 10.0, cuDNN 7.6 on Windows10. 

## Getting Started
Clone the repo:
```
git clone https://gitee.com/seuvcl/CVPR2020-OOH.git
```
Install the requirements using conda:
```
conda create -n occlusion python=3.6
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt
```

## Running the Demo
1) Download the trained model from [here](https://pan.baidu.com/s/1jS5cW_cT2hTTgYxUch4Yow) (extraction code **\[jhwp\]**) and put it in ```trained_model``` folder. 

2) Download the official [SMPL](http://smplify.is.tue.mpg.de/) neutral model. Rename the model to ```SMPL_NEUTRAL.pkl``` and put it in ```data``` folder.

3) To test on your own image, you can edit the ```cfg_files\demo.yaml``` and run:
```
python demo.py --config cfg_files\demo.yaml
```

If you want to fit the SMPL model to the regressed mesh, you can set the ```fitting=True``` in ```cfg_files\demo.yaml``` and then run:
```
python demo.py --config cfg_files\demo.yaml
```

## Dataset
We provide the [3DOH50K dataset](https://pan.baidu.com/s/1j1pYGCoPjlbpIBt0Nn1l_g) (extraction code **\[hb1d\]**), which is the first real 3D human dataset for the problem of human reconstruction and pose estimation in occlusion scenarios.
Visualizing 3DOH50K:
```
python utils/visualize.py --base_dir /PATH/TO/THE/3DOH50K
```
We also provide the full video dataset at [here](https://github.com/boycehbz/CHOMP#ocmotion-dataset).

## Citation
```
@article{huang2022object,
  title={Object-Occluded Human Shape and Pose Estimation with Probabilistic Latent Consistency},
  author={Huang, Buzhen and Zhang, Tianshu and Wang, Yangang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}
```

```
@inproceedings{ooh20,
  title = {Object-Occluded Human Shape and Pose Estimation from a Single Color Image},
  author = {Tianshu, Zhang and Buzhen, Huang and Yangang, Wang},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}
}
```

## References
PyTorch implementation of SMPL model is from [CalciferZh](https://github.com/CalciferZh/SMPL).