  Clothes-changing person re-identification (CC-ReID) aims to retrieve images
of the same person wearing different outfits. Mainstream researches focus on
designing advanced model structures and strategies to capture identity
information independent of clothing. However, the same-clothes discrimination
as the standard ReID learning objective in CC-ReID is persistently ignored in
previous researches. In this study, we dive into the relationship between
standard and clothes-changing~(CC) learning objectives, and bring the inner
conflicts between these two objectives to the fore. We try to magnify the
proportion of CC training pairs by supplementing high-fidelity clothes-varying
synthesis, produced by our proposed Clothes-Changing Diffusion model. By
incorporating the synthetic images into CC-ReID model training, we observe a
significant improvement under CC protocol. However, such improvement sacrifices
the performance under the standard protocol, caused by the inner conflict
between standard and CC. For conflict mitigation, we decouple these objectives
and re-formulate CC-ReID learning as a multi-objective optimization (MOO)
problem. By effectively regularizing the gradient curvature across multiple
objectives and introducing preference restrictions, our MOO solution surpasses
the single-task training paradigm. Our framework is model-agnostic, and
demonstrates superior performance under both CC and standard ReID protocols.
