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https://github.com/MomoAILab/ultrapose
Official repository for the ICCV 2021 paper: UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model.
https://github.com/MomoAILab/ultrapose
Last synced: about 6 hours ago
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Official repository for the ICCV 2021 paper: UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model.
- Host: GitHub
- URL: https://github.com/MomoAILab/ultrapose
- Owner: MomoAILab
- License: mit
- Created: 2021-10-10T09:46:05.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-03T03:33:10.000Z (about 3 years ago)
- Last Synced: 2024-08-03T17:12:30.546Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 14.2 MB
- Stars: 101
- Watchers: 14
- Forks: 12
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model
Official repository for the **ICCV 2021** paper:
**UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model** [[PDF](https://openaccess.thecvf.com/content/ICCV2021/papers/Yan_UltraPose_Synthesizing_Dense_Pose_With_1_Billion_Points_by_Human-Body_ICCV_2021_paper.pdf)]
Haonan Yan, Jiaqi Chen, Xujie Zhang, Shengkai Zhang, Nianhong Jiao, Xiaodan Liang, Tianxiang Zheng
The dataset is now available at [Baidu net disk](https://pan.baidu.com/s/1lHM_6bxWGp5ZrxFQNcJpTg) (code: bpi2) or [google drive](https://drive.google.com/drive/folders/13SNcjuQBT62JfCGgBlchnLqbX8cv4QxT?usp=sharing).
## Introduction
In this work, we introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Furthermore, we build a data generation system based on this decoupling 3D model, and construct an ultra dense synthetic benchmark UltraPose, containing around 1.3 billion corresponding points.
## Installation
We recommend creating a clean [conda](https://docs.conda.io/) environment and install all dependencies.
You can do this as follows:
step1
```
conda create -n ultrapose python=3.7
conda activate ultrapose
```
step2
```
conda install pytorch=1.7.1 torchvision cudatoolkit=10.2 -c pytorch
```
step3
```
pip install ml-collections opencv-python imgaug visdom pycocotools Cython future h5py
```
You need to build python3 densepose for evaluation. You can do this as follows:
```
cd $UltraPoseDir/eval
make
cd $UltraPoseDir/eval/DensePoseData
bash get_eval_data.sh
```
## Training
For single GPU training, please use default configurations by running:
```
python train.py --dataroot data/ultrapose
```
Besides, you can also use visdom to monitor the training process.
```
python -m visdom.server
python train.py --dataroot data/ultrapose --use_visdom
```
For multi-GPU training with default configurations, you can modify `train_transformer.sh` accordingly and run:
```
sh train_transformer.sh
```
## Evaluation
```
python evaluation.py
```
## Dataset
The dataset is now available from [Baidu net disk](https://pan.baidu.com/s/1lHM_6bxWGp5ZrxFQNcJpTg) (code: bpi2) or [google drive](https://drive.google.com/drive/folders/13SNcjuQBT62JfCGgBlchnLqbX8cv4QxT?usp=sharing).
Extract the data and put them under `$UltraPoseDir/data`.
| Dataset | Persons | Points | #Avg Density | Mask Resolution | No error |
| :----: | :----: | :----: |:----: | :----: | :----: |
| Densepose-COCO | 49K | 5.2M | 106 | 256x256 | |
| UltraPose | 5K | 13M | 2.6K | 512x512 | ✓ |
## Acknowledgements
Parts of the code are taken or adapted from the following repos:
- [TransUNet](https://github.com/Beckschen/TransUNet)
- [ViT-pytorch](https://github.com/jeonsworld/ViT-pytorch)
- [vposer](https://github.com/nghorbani/human_body_prior)
- [densepose](https://github.com/facebookresearch/DensePose)
- [densepose_python3](https://github.com/stimong/densepose_python3)
## Citation
If you use this code or Ultrapose for your research, please cite our work:
```
@inproceedings{yan2021ultrapose,
title={UltraPose: Synthesizing Dense Pose With 1 Billion Points by Human-Body Decoupling 3D Model},
author={Yan, Haonan and Chen, Jiaqi and Zhang, Xujie and Zhang, Shengkai and Jiao, Nianhong and Liang, Xiaodan and Zheng, Tianxiang},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={10891--10900},
year={2021}
}
```