Long-Term Person Re-Identification (LT-ReID) has become increasingly crucial in computer vision and biometrics. In this work, we aim to extend LT-ReID beyond pedestrian recognition to include a wider range of real-world human activities while still accounting for cloth-changing scenarios over large time gaps. This setting poses additional challenges due to the geometric misalignment and appearance ambiguity caused by the diversity of human pose and clothing. To address these challenges, we propose a new approach 3DInvarReID for (i) disentangling identity from non-identity components (pose, clothing shape, and texture) of 3D clothed humans, and (ii) reconstructing accurate 3D clothed body shapes and learning discriminative features of naked body shapes for person ReID in a joint manner. To better evaluate our study of LT-ReID, we collect a real-world dataset called CCDA, which contains a wide variety of human activities and clothing changes. Experimentally, we show the superior performance of our approach for person ReID.

翻译：长期行人重识别（LT-ReID）在计算机视觉和生物特征识别领域日益重要。本研究旨在将LT-ReID从行人识别扩展到更广泛的现实人类活动场景，同时考虑大时间跨度下衣物更换的情境。由于人体姿态和服装多样性导致的几何错位和外观模糊，该设定带来了额外挑战。为解决这些问题，我们提出新方法3DInvarReID，用于：(i) 从三维穿衣人体的非身份成分（姿态、服装形状和纹理）中解耦身份信息；(ii) 联合重建精确的三维穿衣人体形状并学习裸体形状的判别性特征以实现行人重识别。为更好评估长期行人重识别研究，我们收集了包含多样化人类活动和衣物更换的真实世界数据集CCDA。实验表明，该方法在行人重识别任务中展现出优越性能。