Generalizable Person Re-Identification From a 3D Perspective: Addressing Unpredictable Viewpoint Changes

Abstract

Most existing Domain Generalizable Person Re-identification (DG-ReID) methods focus on addressing style disparities between domains but often overlook the impact of unpredictable camera view changes, which we have identified as a significant factor responsible for poor generalization performance. To address this issue, we propose a novel approach from a 3D perspective, utilizing a customized 2D-to-3D reconstruction model to convert images captured from arbitrary camera views into canonical view images. However, merely applying a 3D reconstruction model in isolation may not result in improved DG-ReID performance, as reconstruction quality can be influenced by multiple factors, such as insufficient image resolution, extreme viewpoint, and environmental variations. These factors may lead to error accumulation and the loss of critical discriminative clues in the reconstructed results. To address this difficulty, we propose fusing the canonical view image with the original image using a transformer-based module. The transformer’s cross-attention mechanism is ideal for aligning and fusing the key semantic clues of the original image with the canonical view image, compensating for reconstruction errors. We demonstrate the effectiveness of our method through extensive experiments in various evaluation settings, achieving superior DG-ReID performance compared to existing approaches. Our approach addresses the impact of unpredictable camera view changes and provides a new perspective for designing DG-ReID methods.