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Existing gait recognition benchmarks mostly include minor clothing variationsin the laboratory environments, but lack persistent changes in appearance overtime and space. In this paper, we propose the first in-the-wild benchmarkCCGait for cloth-changing gait recognition, which incorporates diverse clothingchanges, indoor and outdoor scenes, and multi-modal statistics over 92 days. Tofurther address the coupling effect of clothing and viewpoint variations, wepropose a hybrid approach HybridGait that exploits both temporal dynamics andthe projected 2D information of 3D human meshes. Specifically, we introduce aCanonical Alignment Spatial-Temporal Transformer (CA-STT) module to encodehuman joint position-aware features, and fully exploit 3D dense priors via aSilhouette-guided Deformation with 3D-2D Appearance Projection (SilD) strategy.Our contributions are twofold: we provide a challenging benchmark CCGait thatcaptures realistic appearance changes across an expanded and space, and wepropose a hybrid framework HybridGait that outperforms prior works on CCGaitand Gait3D benchmarks. Our project page is available athttps://github.com/HCVLab/HybridGait.

HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations