https://github.com/upc-virvig/dragposer
Official Code for Eurographics 2025 paper "DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization"
https://github.com/upc-virvig/dragposer
deep-learning full-body-tracking human-pose-estimation human-pose-tracking inverse-kinematics motion-capture unity
Last synced: about 3 hours ago
JSON representation
Official Code for Eurographics 2025 paper "DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization"
- Host: GitHub
- URL: https://github.com/upc-virvig/dragposer
- Owner: UPC-ViRVIG
- License: mit
- Created: 2025-01-04T14:04:18.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-17T10:38:32.000Z (8 months ago)
- Last Synced: 2025-02-17T11:32:33.436Z (8 months ago)
- Topics: deep-learning, full-body-tracking, human-pose-estimation, human-pose-tracking, inverse-kinematics, motion-capture, unity
- Language: Python
- Homepage: https://upc-virvig.github.io/DragPoser/
- Size: 19 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization
:warning: Under construction... :construction: :construction:
## Getting Started
1. Clone the repository onto your local system.
2. Navigate to the `python` directory.
3. Create a virtual environment with: ``python -m venv env`` (tested on Python 3.9).
4. Activate the created virtual environment.
5. Install the necessary packages from the requirements file with: ``pip install -r requirements.txt``.
6. Download and install [PyTorch](https://pytorch.org/get-started/locally/).
### Evaluate
**One BVH file**
```bash
python .\src\eval_drag.py .\models\model_dancedb .\data\example\eval\example.bvh --config .\config\6_trackers_config.json
```
**A directory with BVH files**
```bash
python .\src\eval_drag.py .\models\model_dancedb .\data\example\eval\ --config .\config\6_trackers_config.json
```
Results will be saved in ``.\data\``
### Train
Training and evaluation data should be in a directory similar to ``.\data\example\``
Inside that directory there must be two files (eval and train) with the .bvh files for training.
Note that the included ``.\data\example\`` does not have enough data for training, it only includes an example file from the preprocessed AMASS dataset.
**1. Train the VAE**
```bash
python .\src\train.py .\data\example\ name --fk
```
**2. Train the temporal predictor**
```bash
python .\src\train_temporal.py .\data\example\ name
```
## License
- The code in this repository is released under the MIT license. Please, see the [LICENSE](LICENSE) for further details.
- The model weights in this repository and the data are licensed under CC BY-SA 4.0 license as found in the [LICENSE_data](LICENSE_data) file.
- The webpage under the ``.docs\`` directory was built using the [Academic Project Page Template](https://github.com/eliahuhorwitz/Academic-project-page-template). This website is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.