https://github.com/googleinterns/humangps
https://github.com/googleinterns/humangps
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/googleinterns/humangps
- Owner: googleinterns
- License: apache-2.0
- Created: 2021-05-12T13:57:28.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-06-26T04:29:25.000Z (almost 5 years ago)
- Last Synced: 2025-04-06T18:50:40.627Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 33.4 MB
- Stars: 60
- Watchers: 3
- Forks: 10
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
Tensorflow implementation of the paper "HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences", CVPR 2021.
### [Project Page](https://feitongt.github.io/HumanGPS/) | [Videos](https://feitongt.github.io/HumanGPS/#Vis_feature) | [Paper](https://arxiv.org/pdf/2103.15573.pdf) | [Data]()
## Setup
* Python 3.6
* TensorFlow 2.0
* Tensorflow-Addon
* gin-config
* scikit-learn
```sh
pip install -r requirements.txt --user
pip install gdown
```
## Running code
Here we show how to run our code on SMPL intra and inter testing data. You can download the rest of the synthetic SMPL testing data used in the paper [here]().
### 1. Download pretrained model.
```sh
bash download_model.sh
```
### 2. Evaluate on intra testing data.
(a) Run
```sh
mkdir -p ./test_data/
```
Download our SMPL intra test data from [smpl_intra_data](https://www.icloud.com/iclouddrive/0UmpxsQ3btifW8F67ENT6chng#smpl%5Fintra%5Ftest) in `./test_data/`
To evaluate average epe on intra test dataset.
(b) set `JOB_NAME="eval_optical_flow_intra"` in `./script/inference_local.sh`
(c) Run
```sh
bash ./script/inference_local.sh
```
### 3. Evaluate on inter testing data.
(a) Run
```sh
mkdir -p ./test_data/
```
Download our SMPL inter test data from [smpl_inter_data](https://www.icloud.com/iclouddrive/0fC-vqM9UCK-kl2KA56f4OwqA#smpl%5Finter%5Ftest) in `./test_data/`
To evaluate average epe on inter test dataset.
(b) set `JOB_NAME="eval_optical_flow_inter"` in `./script/inference_local.sh`
(c) Run
```sh
bash ./script/inference_local.sh
```
### 4. Train on intra testing data.
Currently, we can not provide the whole training dataset due to the copyright and huge size of the data.
Here, we provide an example configuration for training on intra testing data.
```sh
bash ./script/train_local.sh
```
### 5. Inference on toy examples for visualization.
Check out `./inference_demo.ipynb` for toy examples.
## Citation
If you find this code useful in your research, please cite:
```bibtex
@inproceedings{tan2021humangps,
title = {{HumanGPS: Geodesic PreServing Feature for Dense Human Correspondence}},
author = {Tan, Feitong and Tang, Danhang and Dou, Mingsong and Guo, Kaiwen and Pandey, Rohit and Keskin, Cem and Du, Ruofei and Sun, Deqing and Bouaziz, Sofien and Fanello, Sean and Tan, Ping and Zhang, Yinda},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021},
publisher = {IEEE},
}
```