---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- human-trajectory-prediction
- jrdb
- jta
- physics-simulation
- paa2026
pipeline_tag: other
library_name: pytorch
---

# Hexa-P: PAA 2026 release assets

Checkpoints and preprocessed data for
**Hexa-P: Physical Plausibility-aware Pre-training for Pedestrian Path Prediction** (Pattern Analysis and Applications, 2026).

🐙 [code](https://github.com/ImIntheMiddle/Hexa-P) · 🌐 [project page](https://iminthemiddle.github.io/Hexa-P-Page/)

## Contents

- `weights/stage1/hexap_pretrain.ckpt`: Hexa-P pre-trained in the physics simulation (5 hypotheses), the starting point of fine-tuning
- `weights/stage1/hexap_pretrain_deterministic.ckpt`: the same with one hypothesis
- `weights/stage1/controller.ckpt`: humanoid controller that walks the pre-training paths
- `weights/stage2/{jrdb, jta}/hexap.pt`: Hexa-P fine-tuned on JRDB and JTA
- `data/{jta, jrdb, vru}/{train, val, test}/`: preprocessed data (`vru` is the Urban dataset)
- `data/jta_replay/test/`: JTA test scenes for the robot navigation experiment

## Usage

See the [GitHub README](https://github.com/ImIntheMiddle/Hexa-P) for download + setup instructions.

## License

Research, non-commercial use only. `data/<dataset>/LICENSE` gives the licence of each folder: JTA CC BY-NC 4.0, JRDB CC BY-NC-SA 4.0, Urban CC BY 4.0. The weights were trained with SMPL and AMASS, which restrict commercial use. The JTA authors require the statement: "I declare that I will use the JTA Dataset for research and educational purposes only, since I am aware that commercial use is prohibited. I also undertake to purchase a copy of Grand Theft Auto V."

## Citation

```bibtex
@article{taketsugu2026hexap,
  title   = {Hexa-P: Physical Plausibility-aware Pre-training for Pedestrian Path Prediction},
  author  = {Taketsugu, Hiromu and Nobuhara, Shohei and Ukita, Norimichi},
  journal = {Pattern Analysis and Applications},
  year    = {2026}
}
```