Occlusion-Resilient Category-Agnostic Pose Estimation with Conditional Flow Matching

Category-Agnostic Pose Estimation (CAPE) aims to local-ize category-specific keypoints on a query image from only a few anno-tated support examples. Existing CAPE methods are largely based ondirect coordinate regression or local heatmap matching, which becomeunreliable under severe occlusion because missing keypoints cannot berecovered from local evidence alone. We present FlowCape, a condi-tional flow-matching framework for occlusion-resilient CAPE. FlowCapefirst extracts query-image features and combines them with keypoint-description embeddings in a Heatmap-Guided Initialization module, pro-ducing a stable initial pose for transport. A shared Riemannian PoseHead then predicts the conditional velocity field, while its internal GraphFlow Encoder fuses query image, textual keypoint semantics, and skele-ton topology to enforce structured pose evolution. The final predictionis obtained by probability-flow ODE rollout, and training is driven byhybrid flow-matching supervision together with initialization and roll-out consistency constraints. To better evaluate robustness under severeocclusion, we further introduce Occ80, an occlusion-focused benchmarkspanning 80 categories. Experiments on MP-100 and Occ80 show thatFlowCape consistently outperforms strong CAPE baselines, reach thestate-of-the-art under occluded scenario.