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扩展到行人识别之外，涵盖更广泛的人类真实活动场景，同时考虑长时间间隔内的换装情况。由于人体姿态和服装多样性导致的几何错位与外观歧义，该设定带来了额外挑战。为解决这些问题，我们提出了一种新方法3DInvarReID，用于：(i) 将三维着装人体的身份特征与非身份特征（姿态、服装形状和纹理）进行解耦；(ii) 联合重建精确的三维着装人体形状，并学习用于行人重识别的裸体形状判别特征。为更好评估我们的LT-ReID研究，我们收集了名为CCDA的真实世界数据集，其中包含丰富的人类活动类型和服装变化。实验结果表明，我们的方法在行人重识别任务中具有优越性能。