https://github.com/GUO-W/MultiMotion

Multi-Person Extreme Motion Prediction
https://github.com/GUO-W/MultiMotion

Last synced: about 2 months ago
JSON representation

Multi-Person Extreme Motion Prediction

• Host: GitHub
• URL: https://github.com/GUO-W/MultiMotion
• Owner: GUO-W
• License: gpl-3.0
• Created: 2021-12-07T13:10:02.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-10T13:07:51.000Z (over 2 years ago)
• Last Synced: 2024-04-05T10:32:57.073Z (6 months ago)
• Language: Python
• Size: 48.8 KB
• Stars: 46
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-conditional-content-generation - [Code

README

        

### Multi-Person Extreme Motion Prediction

Implementation for paper

Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda, Francesc Moreno-Noguer,

[Multi-Person Extreme Motion Prediction](https://arxiv.org/abs/2105.08825), CVPR2022. 

[[paper](https://arxiv.org/abs/2105.08825)] [[Project page](https://team.inria.fr/robotlearn/multi-person-extreme-motion-prediction/)]

---

### Dependencies

This repo been tested on CUDA9, Python3.6, Pytorch1.5.1.

---

### Directory

```

ROOT

|-- datasets

    |-- pi

        |-- acro1

        `-- acro2

|-- run_exps

|-- main

|-- model

|-- utils 

|-- checkpoint

    |--pretrain_ckpt

|-- tensorboard

`-- outputs

```

---

### Preparing data

Please request and download data from [ExPI](https://zenodo.org/record/5578329#.YbjaLPHMK3J) and put the data in /datasets.

*Note: If you are NOT affiliated with an institution from a country offering an adequate level of data protection 

(most countries without EU, please check [the list](https://ec.europa.eu/info/law/law-topic/data-protection/international-dimension-data-protection/adequacy-decisions_en)), you have to sign the "Standard Contractual Clauses" when applying for the data. Please follow the instructions in the downloading website.*

---

### Test on our pretrained models

* Please download pretrained models from [model](https://drive.google.com/drive/folders/1TVgaVi_SaQl9j_KoGaa3XZb5XjGC7e0Y?usp=sharing)

and put them in ./checkpoint/pretrain_ckpt/.

* Run ./run_exps/run_pro1.sh to test on Common-Action-Split. 

(To test on Single-Action-Split and Unseen-Action-Split, please run run_pro2.sh and run_pro3.sh respectively.)

---

### Training and testing

* To train/test on Common-Action-Split, please look at ./run_exps/run_pro1.sh and uncommand the corresponding lines.

* When testing, '--save_result' option could be used to save the result of different experiments in a same file ./outputs/results.json. 

Than ./outputs/write_results.py could be used to easily generate the result table as shown in our paper.

* Same for Single-Action-Split/Unseen-Action-Split.

---

### Citing

If you find our code or data helpful, please cite our work

@article{guo2021multi,

    title={Multi-Person Extreme Motion Prediction}, 

    author={Wen,Guo and Xiaoyu, Bie and Xavier, Alameda-Pineda, Francesc,Moreno-Noguer}, 

    journal={arXiv preprint arXiv:2105.08825}, 

    year={2021} }

---

### Acknowledgments

Some codes are adapted from [HisRepItself](https://github.com/wei-mao-2019/HisRepItself).

---

### Licence

GPL