GPS: Geometry-Guided Patch Synthesis for Aerial-Ground Person Re-Identification

Aerial-ground person re-identification must match identities despite substantial changes in local appearance and visibility across viewpoints. Existing methods primarily modify or align observed images, but do not explicitly construct identity-preserving cross-view variations with controllable matching difficulty. In this paper, we propose Geometry-Guided Patch Synthesis (GPS), a feature-level hard-positive learning framework that synthesizes structured changes in local identity evidence during training. Specifically, a Geometry-Constrained Patch Synthesizer (GCPS) module uses a fixed spatial coordinate prior to reorganize patch tokens through three complementary operations: compression, merging, and vanishing, thereby producing candidates with controlled local variations. Then, a Hard-Positive Patch Synthesis (HPPS) module is proposed to select the hardest identity-consistent candidate within a prescribed difficulty range relative to a real same-identity cross-view observation. Finally, a Dual-Path Evidence Fusion (DPEF) module transfers locally supported correspondence between the original and selected synthetic features into identity learning while preserving unsupported original evidence. Extensive experiments on four aerial-ground benchmarks demonstrate the effectiveness of GPS, with consistent improvements over Plain CLIP across all evaluated protocols. Component ablations further demonstrate the complementary contributions of structured patch synthesis, difficulty-controlled selection, and evidence fusion. The code will be made publicly available.