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.
Deep high-resolution representation learning for human pose estimation
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cs.CV 2years
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A new method accumulates historical pose features across layers in a Transformer network to reach state-of-the-art 3D human pose estimation accuracy.
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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L2A: Learning to Accumulate Pose History for Accurate 3D Human Pose Estimation
A new method accumulates historical pose features across layers in a Transformer network to reach state-of-the-art 3D human pose estimation accuracy.