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MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities
Gait recognition is commonly studied using RGB videos or their derived silhouettes and poses. Yet human walking produces heterogeneous photometric, geometric, and motion cues that cannot be systematically examined with RGB-centered benchmarks. We present MMGait, a large-scale multi-sensor benchmark that brings visible, infrared, depth, LiDAR, and radar observations into sequence-level correspondence. It provides diverse modalities spanning appearance, contours, geometry, motion, and body structure. Under a shared impostor-augmented protocol, we evaluate single-modal recognition, cross-modal re
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- arXiv · AI, language, vision and robotics · 2026-09-10T14:23:29.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.