UST-Hand is a self-supervised 3D hand pose estimation method using conditional normalizing flows for uncertainty-aware hypothesis sampling and probabilistic point cloud interactions to achieve up to 37.8% better MPVPE than prior self-supervised approaches on three datasets.
Mhformer: Multi-hypothesis transformer for 3d human pose estimation
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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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UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
UST-Hand is a self-supervised 3D hand pose estimation method using conditional normalizing flows for uncertainty-aware hypothesis sampling and probabilistic point cloud interactions to achieve up to 37.8% better MPVPE than prior self-supervised approaches on three datasets.
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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.