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The Vitruvian Manifold: Inferring Dense Correspondences for One-Shot Human Pose Estimation

Jonathan Taylor, Jamie Shotton, Toby Sharp, and Andrew Fitzgibbon

Abstract

Fitting an articulated model to image data is often approached as an optimization over both model pose and model-to-image correspondence. For complex models such as humans, previous work has required a good initialization, or an alternating minimization between correspondence and pose. In this paper we investigate one-shot pose estimation: can we directly infer correspondences using a regression function trained to be invariant to body size and shape, and then optimize the model pose just once? We evaluate on several challenging single-frame data sets containing a wide variety of body poses, shapes, torso rotations, and image cropping. Our experiments demonstrate that one-shot pose estimation achieves state of the art results and runs in real-time.

Details

Publication typeInproceedings
Published inProc. CVPR
PublisherIEEE
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