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MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities

Paper recorded by Signals 4 on 2026-09-10 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.CV · 计算机视觉 · first seen 2026-09-11

Abstract

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 corresponden

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#91 most recent of 237 cs.CV papers we have recorded · ↑ newer: LangStreet: Persistent Language Fields for Anchor-Decoded Street Gauss · ↓ older: OmniKVQuant: KV Cache Quantization for Omni-LLMs
Cite this page: MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities: the #91 most recent of 237 cs.CV papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mmgait-benchmarking-and-unifying-gait-recognition-across-heterogeneous-modalitie.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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