Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916659201048576 |
|---|---|
| author | Taketsugu, Hiromu Oba, Takeru Maeda, Takahiro Nobuhara, Shohei Ukita, Norimichi |
| author_facet | Taketsugu, Hiromu Oba, Takeru Maeda, Takahiro Nobuhara, Shohei Ukita, Norimichi |
| contents | Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility of the predicted trajectory by locomotion generation under the laws of physics. While the plausibility of locomotion is learned with an indifferentiable physics simulator, it is replaced by our differentiable Locomotion Value function to train an HTP network in a data-driven manner. In particular, our proposed Embodied Locomotion loss is beneficial for efficiently training a stochastic HTP network using multiple heads. Furthermore, the Locomotion Value filter is proposed to filter out implausible trajectories at inference. Experiments demonstrate that our method enhances even the state-of-the-art HTP methods across diverse datasets and problem settings. Our code is available at: https://github.com/ImIntheMiddle/EmLoco. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment Taketsugu, Hiromu Oba, Takeru Maeda, Takahiro Nobuhara, Shohei Ukita, Norimichi Computer Vision and Pattern Recognition Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility of the predicted trajectory by locomotion generation under the laws of physics. While the plausibility of locomotion is learned with an indifferentiable physics simulator, it is replaced by our differentiable Locomotion Value function to train an HTP network in a data-driven manner. In particular, our proposed Embodied Locomotion loss is beneficial for efficiently training a stochastic HTP network using multiple heads. Furthermore, the Locomotion Value filter is proposed to filter out implausible trajectories at inference. Experiments demonstrate that our method enhances even the state-of-the-art HTP methods across diverse datasets and problem settings. Our code is available at: https://github.com/ImIntheMiddle/EmLoco. |
| title | Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.17267 |