Bridging the Visual-to-Physical Gap: Physically Aligned Representations for Fall Risk Analysis
Vision-based fall analysis has advanced rapidly, but a key bottleneck remains: visually similarmotions can correspond to very different physical outcomes because small differences in contactmechanics and protective responses are hard to infer from appearance alone. Most existingapproaches handle this by supervised injury prediction, which depends on reliable injury labels.In practice, such labels are difficult to obtain: video evidence is often ambiguous (occlusion,viewpoint limits), and true injury events are rare and cannot be safely staged, leading to noisysupervision. We address this problem with PHARL (PHysics-aware Alignment RepresentationLearning), which learns physically meaningfu...
| Authors | Xianqi Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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