PoseBridge recovers semantic information lost during skeletonization by extracting pose-anchored cues from human pose estimation and transferring them via skeleton-conditioned bridging and semantic prototype adaptation, yielding 13.3-17.4 point gains on the Kinetics PURLS benchmark.
Skeleton based zero shot action recognition in joint pose-language semantic space
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A Transformer model with unified skeleton representation, two-stream motion encoder, and multi-grained motion-text contrastive alignment achieves effective recognition on a new integrated heterogeneous open-vocabulary skeleton dataset.
citing papers explorer
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PoseBridge: Bridging the Skeletonization Gap for Zero-Shot Skeleton-Based Action Recognition
PoseBridge recovers semantic information lost during skeletonization by extracting pose-anchored cues from human pose estimation and transferring them via skeleton-conditioned bridging and semantic prototype adaptation, yielding 13.3-17.4 point gains on the Kinetics PURLS benchmark.
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Towards Universal Skeleton-Based Action Recognition
A Transformer model with unified skeleton representation, two-stream motion encoder, and multi-grained motion-text contrastive alignment achieves effective recognition on a new integrated heterogeneous open-vocabulary skeleton dataset.