  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.
