PuppetGait: Generalizing Gait Recognition via 3D Body-Aligned LVM Features

Large vision model (LVM)-based gait recognition has achieved remarkable performance. However, current LVM-based methods largely rely on 2D image-plane representations and often neglect the underlying 3D structure of the human body. Without 3D structural guidance, 2D representations are sensitive to viewpoint variations. This sensitivity leads to unreliable gait features and limits generalizability in real-world 3D scenarios. To bridge this gap, we propose PuppetGait, a framework that aligns LVM features into a unified 3D canonical body space. The method conceptually manipulates the features like a puppet, adjusting them to a predefined pose and viewpoint. Comprehensive evaluations on CCPG, CCGR-MINI, CASIA-B*, and SUSTech1K show that PuppetGait maintains strong within-domain performance (96.54% mean Rank-1 on CCPG) while substantially improving cross-domain generalization, reaching 23.22% Rank-1 on CCPG-to-CCGR-MINI and 63.28% mean Rank-1 on CCGR-MINI-to-CCPG. Code will be publicly available.