STAR-Pose combines a spatial-temporal transformer with linear attention, a CNN texture branch, and a pose-aware loss to estimate human pose from low-resolution video, reporting up to 5.2% mAP improvement at 64x48.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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STAR-Pose: Efficient Low-Resolution Video Human Pose Estimation via Spatial-Temporal Adaptive Super-Resolution
STAR-Pose combines a spatial-temporal transformer with linear attention, a CNN texture branch, and a pose-aware loss to estimate human pose from low-resolution video, reporting up to 5.2% mAP improvement at 64x48.