Abstract
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 this https URL.
Abstract (translated)
现有的步伐识别基准通常包括实验室环境中的轻微服装变化，但缺乏随时间和空间持续变化的视觉效果。在本文中，我们提出了第一个在野外的衣物更换的CCGait基准，该基准包括多样化的服装变化、室内和室外场景以及超过92天的多模态统计。为了进一步解决衣物和观点变化的影响，我们提出了HybridGait混合方法，该方法利用了时间和3D人体网格的投影2D信息。具体来说，我们引入了一个规范的alignment-spatial-temporalTransformer（CA-STT）模块来编码人类关节位置相关的特征，并完全利用通过3D-2D外观投影策略指导的轮廓引导变形。我们的贡献是双重的：我们提供一个挑战性的衣物更换基准，涵盖了扩展和空间的现实外观变化，并提出了一种混合框架HybridGait，在CCGait和Gait3D基准上超过了以前的工作。我们的项目页面可以通过这个链接获得。
URL
https://arxiv.org/abs/2401.00271
