A new method accumulates historical pose features across layers in a Transformer network to reach state-of-the-art 3D human pose estimation accuracy.
Exploiting temporal contexts with strided transformer for 3d human pose estimation.IEEE Transactions on Multimedia, 25:1282–1293
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DT-Pose reformulates WiFi HPE as domain-consistent representation learning via temporal contrastive masked pretraining plus hybrid topology-constrained decoding to yield more accurate and realistic 2D/3D poses.
citing papers explorer
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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.
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Towards Robust and Realistic Human Pose Estimation via WiFi Signals
DT-Pose reformulates WiFi HPE as domain-consistent representation learning via temporal contrastive masked pretraining plus hybrid topology-constrained decoding to yield more accurate and realistic 2D/3D poses.