Current Visible-Infrared Person Re-Identification (VI-ReID) methods prioritize extracting distinguishing appearance features, ignoring the natural resistance of body shape against modality changes. Initially, we gauged the discriminative potential of shapes by a straightforward concatenation of shape and appearance features. However, two unresolved issues persist in the utilization of shape features. One pertains to the dependence on auxiliary models for shape feature extraction in the inference phase, along with the errors in generated infrared shapes due to the intrinsic modality disparity. The other issue involves the inadequately explored correlation between shape and appearance features. To tackle the aforementioned challenges, we propose the Shape-centered Representation Learning framework (ScRL), which focuses on learning shape features and appearance features associated with shapes. Specifically, we devise the Shape Feature Propagation (SFP), facilitating direct extraction of shape features from original images with minimal complexity costs during inference. To restitute inaccuracies in infrared body shapes at the feature level, we present the Infrared Shape Restitution (ISR). Furthermore, to acquire appearance features related to shape, we design the Appearance Feature Enhancement (AFE), which accentuates identity-related features while suppressing identity-unrelated features guided by shape features. Extensive experiments are conducted to validate the effectiveness of the proposed ScRL. Achieving remarkable results, the Rank-1 (mAP) accuracy attains 76.1%, 71.2%, 92.4% (72.6%, 52.9%, 86.7%) on the SYSU-MM01, HITSZ-VCM, RegDB datasets respectively, outperforming existing state-of-the-art methods.

翻译：当前可见光-红外行人重识别（VI-ReID）方法优先提取具有区分性的外观特征，忽略了人体形状对模态变化的天然鲁棒性。我们最初通过简单拼接形状与外观特征评估了形状的判别潜力，但在利用形状特征时仍存在两个未解决的关键问题：其一，推理阶段依赖辅助模型提取形状特征，且由于模态固有差异导致生成的红外形状存在误差；其二，形状与外观特征之间的关联性尚未得到充分探索。针对上述挑战，我们提出基于形状中心的表征学习框架（ScRL），该框架专注于学习形状特征及与形状关联的外观特征。具体而言，我们设计了形状特征传播模块（SFP），使其在推理过程中以极低复杂度直接从原始图像提取形状特征。为在特征层面修正红外人体形状的不准确性，我们提出红外形状修正机制（ISR）。此外，为获取与形状关联的外观特征，我们设计了外观特征增强模块（AFE），利用形状特征引导增强身份相关特征并抑制无关特征。大量实验验证了所提ScRL框架的有效性：在SYSU-MM01、HITSZ-VCM、RegDB数据集上，Rank-1（mAP）精度分别达76.1%、71.2%、92.4%（对应mAP为72.6%、52.9%、86.7%），全面超越现有最优方法。