We address the challenge of speed and generalization in sketch-based 3D pose estimation by employing a learn-from-synthesis strategy. Through training on our established synthetic sketch-pose dataset, we present Sketch2PoseNet that efficiently and accurately predicts generalized 3D human poses across various sketch styles.
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