[Oral at NeurIPS 2020] LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

NeurIPS 2020

[Oral at NeurIPS 2020] LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

Nov 08, 2020
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We learn to predict correspondences by creating a self-supervised loop from a source 3D scan to a 3D mesh model. Correspondences are typically constrained to lie on the surface manifold using a 2D surface UV-map, but those have distortions and boundary discontinuities. The key idea here is to instead predict continuous correspondences in 3D to a canonical surface represented implicitly. This creates a differentiable loop without the need for UV-maps or 2D surface parameterizations. Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration in Advances in Neural Information Processing Systems (NeurIPS), 2020.

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