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变得愈发重要。本研究旨在将LT-ReID从行人识别扩展到涵盖更广泛的真实世界人类活动，同时仍能处理长时间间隔内衣物更换的场景。由于人体姿态与服装多样性导致的几何错位和外观模糊，这一设定带来了额外的挑战。为解决这些问题，我们提出了一种新方法3DInvarReID，用于：(i) 从3D着装人体的非身份成分（姿态、服装形状与纹理）中解耦身份信息；(ii) 以联合方式重建精确的3D着装人体形状，并学习裸体形状的判别性特征以用于行人重识别。为更好地评估我们的LT-ReID研究，我们收集了一个名为CCDA的真实世界数据集，其中包含多种人类活动和衣物更换场景。实验表明，该方法在行人重识别任务中展现出优越性能。