Beyond Alignment: A Generative Matching Paradigm via Flow Matching for Zero-Shot Skeleton-Based Action Recognition

Zero-shot skeleton-based action recognition aims to identifyunseen actions via semantic transfer. The existing methods often strug-gle with the topological mismatch between skeleton and text manifolds,leading to loose decision boundaries due to weak constraints and fail-ing to adapt to unseen distributions during static inference. To miti-gate this challenge, we propose ProtoFM, a novel generative frameworkbased on conditional flow matching and prototype guidance. First, in-stead of heterogeneous skeleton-text matching, ProtoFM introduces agenerative matching paradigm that performs homogeneous skeleton andpseudo-skeleton matching by synthesizing features via ODE-based flowintegration. Second, we propose a prototype-guided center loss that an-chors flow-trajectory states to learnable class prototypes, encouragingcompact semantic clusters and improved class separation. Last, at theinference phase, we develop a training-free prototype-guided rectificationstrategy that exploits a global memory bank for progressive test-timerefinement. Extensive experimental results obtained on multiple bench-marking datasets demonstrate the superiority of the proposed ProtoFMmethod over the state-of-the-art approaches.