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Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation
[article]
2017
arXiv
pre-print
State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth map, any given point in the shared latent space can be
arXiv:1702.03431v2
fatcat:rhqacaskzvagxo4ofbwvg7fpku