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The Vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation

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4 Author(s)

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.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on

Date of Conference:

16-21 June 2012