SEAS: ShapE-Aligned Supervision for Person Re-Identification

We introduce SEAS, using ShapE-Aligned Supervision, to enhance appearance-based person re-identification. When recognizing an individual's identity, existing methods primarily rely on appearance, which can be influenced by the background environment due to a lack of body shape awareness. Although some methods attempt to incorporate other modalities, such as gait or body shape, they encode the additional modality separately, resulting in extra computational costs and lacking an inherent connection with appearance. In this paper, we explore the use of implicit 3-D body shape representations as pixel-level guidance to augment the extraction of identity features with body shape knowledge, in addition to appearance. Using body shape as supervision, rather than as input, provides shape-aware enhancements without any increase in computational cost and delivers coherent integration with pixel-wise appearance features. Moreover, for video-based person re-identification, we align pixel-level features across frames with shape awareness to ensure temporal consistency. Our results demonstrate that incorporating body shape as pixel-level supervision reduces rank-1 errors by 32.8% for frame-based and by 27.2% for video-based re-identification tasks, respectively, and can also be generalized to other existing appearance-based person re-identification methods.