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

本文提出了第一个针对衣着变化的步态识别CCGait自然场景基准，该基准包括室内外环境中不同的衣着变化和多模态统计数据，并通过引入了混合框架HybridGait来解决衣着和视角变化的耦合效应。我们的贡献是：提供了一个具有挑战性的CCGait基准，捕捉了扩展的时间和空间上的现实外观变化，并提出了一个在CCGait和Gait3D基准上优于以前工作的混合框架HybridGait。

混合步态：以混合探索为特点的空时布料更换步态识别的基准