Beyond Motion Pattern: An Empirical Study of Physical Forces for Human Motion Understanding

2025-12-23

AI Summary

Existing vision-based human motion understanding methods largely neglect biomechanical physical cues—such as joint actuation forces—thereby limiting robustness under challenging conditions like occlusion and viewpoint variation. This work is the first to systematically investigate how joint actuation forces enhance action recognition, gait recognition, and fine-grained video captioning. We propose a kinematics-based inverse dynamics method to infer these forces and integrate them into mainstream architectures—including CTR-GCN, spatiotemporal Transformers, and Qwen2.5-VL. Extensive cross-task evaluation across eight benchmarks demonstrates consistent improvements: up to +3.0% in Rank-1 accuracy for side-view gait recognition, +6.96% in accuracy for high-load action recognition, and +0.029 in ROUGE-L for video captioning. Gains are especially pronounced under occlusion (e.g., wearing coats) and side-view conditions. Our results establish a novel paradigm for effectively fusing biomechanical priors with purely visual models.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Human motion understanding has advanced rapidly through vision-based progress in recognition, tracking, and captioning. However, most existing methods overlook physical cues such as joint actuation forces that are fundamental in biomechanics. This gap motivates our study: if and when do physically inferred forces enhance motion understanding? By incorporating forces into established motion understanding pipelines, we systematically evaluate their impact across baseline models on 3 major tasks: gait recognition, action recognition, and fine-grained video captioning. Across 8 benchmarks, incorporating forces yields consistent performance gains; for example, on CASIA-B, Rank-1 gait recognition accuracy improved from 89.52% to 90.39% (+0.87), with larger gain observed under challenging conditions: +2.7% when wearing a coat and +3.0% at the side view. On Gait3D, performance also increases from 46.0% to 47.3% (+1.3). In action recognition, CTR-GCN achieved +2.00% on Penn Action, while high-exertion classes like punching/slapping improved by +6.96%. Even in video captioning, Qwen2.5-VL's ROUGE-L score rose from 0.310 to 0.339 (+0.029), indicating that physics-inferred forces enhance temporal grounding and semantic richness. These results demonstrate that force cues can substantially complement visual and kinematic features under dynamic, occluded, or appearance-varying conditions.

Problem

• Incorporating physical forces improves gait recognition accuracy
• Force cues enhance action recognition, especially for high-exertion movements
• Physics-inferred forces boost video captioning by enriching temporal grounding

Innovation

• Incorporating physical forces into motion understanding pipelines
• Systematically evaluating forces across gait and action recognition tasks
• Enhancing performance under challenging dynamic and occluded conditions