PhysMoP

KO | EN

gitlite — search

#python #hacktoberfest #react #ai #llm #go #golang #linux #windows #awesome #kubernetes #pytorch #mcp #ios

(★ 38)

Code repository for Incorporating Physics Principles for Precise Human Motion Prediction, WACV2024

PhysMoP 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# PhysMoP: Incorporating Physics Principles for Precise Human Motion Prediction

**Incorporating Physics Principles for Precise Human Motion Prediction** <br />
  [Yufei Zhang](https://zhangy76.github.io/), Jeffrey O. Kephart, Qiang Ji <br /> 
  WACV2024, [Paper](https://openaccess.thecvf.com/content/WACV2024/papers/Zhang_Incorporating_Physics_Principles_for_Precise_Human_Motion_Prediction_WACV_2024_paper.pdf) | [SupMat](https://openaccess.thecvf.com/content/WACV2024/supplemental/Zhang_Incorporating_Physics_Principles_WACV_2024_supplemental.pdf) <br />
![](https://raw.githubusercontent.com/zhangy76/PhysMoP/main/method.png)

This repository includes the training and evaluation code for the above paper.

## Installation
### Environment
```bash
conda create -n physmop python=3.8
conda activate physmop
pip install -r requirements.txt
```

## PhysMoP Model Training & Evaluation
Please follow the procedure below to conduct the experiments on AMASS.
### Data Preparation
Download the raw 3D motion sequence data from [AMASS](https://amass.is.tue.mpg.de/) (should be similar for [Human3.6M](https://drive.google.com/drive/folders/1ySxiuTCSdUEqbgTcx7bx02uMglPOkKjc?usp=sharing) and [3DPW](https://virtualhumans.mpi-inf.mpg.de/3DPW/)), and store them under the current directory as 
```
|--dataset
|  |--data_raw
|     |--AMASS
|       |--ACCAD
|       |--BioMotionLab_NTroje
|       |--CMU
|       |-- .
|       |-- .
|       |-- .
|  |--data_processed
```
Preprocess the raw data
```
python AMASS.py
```

### Training
PhysMoP is trained in three steps:
#### 1. Train the data-driven model
```
python train_script.py --name data --data True --num_epochs 5 --keypoint_loss_weight_data 1 --pose_loss_weight_data 2
```
#### 2. Train the physics-based model with the checkpoint (DATA_CHECKPOINT) obtained from step 1.
```
python train_script.py --name data_physics --data True --physics True --pretrained_checkpoint DATA_CHECKPOINT --keypoint_loss_weight_physics_gt 1 --pose_loss_weight_physics_gt 2 --num_epochs 2 --keypoint_loss_weight_data 1 --pose_loss_weight_data 2 --resume
```
#### 3. Fix the data-driven and physics-based model and train the fusion model with the checkpoint (PHYSICS_CHECKPOINT) obtained from step 2.

```
python train_script.py --name data_physics_fusion --data True --physics True --fix_weight True --fusion True --pretrained_checkpoint PHYSICS_CHECKPOINT --keypoint_loss_weight_physics_gt 1 --pose_loss_weight_physics_gt 2 --num_epochs 2 --keypoint_loss_weight_data 1 --pose_loss_weight_data 2 --keypoint_loss_weight_fusion 1 --pose_loss_weight_fusion 2 
```
The saved model checkpoint can be found at './logs'.

### Evaluation
To evaluate a model saved at CHECKPOINT_PATH, please run
```
python eval.py --checkpoint CHECKPOINT_PATH
```

After code re-organization, we re-produced the results as

|  AMASS-Test  |   80ms |  160ms |  320ms |  400ms |  560ms |  720ms |  880ms | 1000ms |
| ------------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
|  MPJPE (mm)  |    0.4 |    2.0 |    9.4 |   14.2 |   24.4 |   36.1 |   48.0 |   61.9 |

The evaluated checkpoint, which includes model weights and training log, can be downloaded via [FINAL_MODEL](https://www.dropbox.com/scl/fi/qfovsy30net1wt2zl04bw/2023_12_21-17_09_24_20364.pt?rlkey=2phz52d26p2w2hbtx50o89c50&dl=0).

## Citation
If you find our work useful, please consider citing the paper:
```bibtex
@InProceedings{Zhang_2024_WACV,
    author    = {Zhang, Yufei and Kephart, Jeffrey O. and Ji, Qiang},
    title     = {Incorporating Physics Principles for Precise Human Motion Prediction},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {6164-6174}
}
```

If you have questions or encouter any issues when running the code, feel free to open an issue or directly contact me via: zhangy76@rpi.edu.

## References
Part of our code is developed based on [siMLPe](https://github.com/dulucas/siMLPe). The SMPL model data is downloaded from [SMPL-X model](https://github.com/vchoutas/smplx). We thank them for generously sharing their outstanding work.

PhysMoP

PhysMoP 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

PhysMoP

PhysMoP 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README