U-mesh: Human Correspondence Matching with Mesh Convolutional Networks

• URL: http://arxiv.org/abs/2108.06695v1
• Date: Sun, 15 Aug 2021 08:58:45 GMT
• Title: U-mesh: Human Correspondence Matching with Mesh Convolutional Networks
• Authors: Benjamin Groisser, Alon Wolf, Ron Kimmel
• Abstract summary: We propose an elegant fusion of regression (bottom-up) and generative (top-down) methods to fit a parametric template model to raw scan meshes. Our first major contribution is an intrinsic convolutional mesh U-net architecture that predicts pointwise correspondence to a template surface. We evaluate the proposed method on the FAUST correspondence challenge where we achieve 20% (33%) improvement over state of the art methods for inter- (intra-) subject correspondence.
• Score: 15.828285556159026
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The proliferation of 3D scanning technology has driven a need for methods to interpret geometric data, particularly for human subjects. In this paper we propose an elegant fusion of regression (bottom-up) and generative (top-down) methods to fit a parametric template model to raw scan meshes. Our first major contribution is an intrinsic convolutional mesh U-net architecture that predicts pointwise correspondence to a template surface. Soft-correspondence is formulated as coordinates in a newly-constructed Cartesian space. Modeling correspondence as Euclidean proximity enables efficient optimization, both for network training and for the next step of the algorithm. Our second contribution is a generative optimization algorithm that uses the U-net correspondence predictions to guide a parametric Iterative Closest Point registration. By employing pre-trained human surface parametric models we maximally leverage domain-specific prior knowledge. The pairing of a mesh-convolutional network with generative model fitting enables us to predict correspondence for real human surface scans including occlusions, partialities, and varying genus (e.g. from self-contact). We evaluate the proposed method on the FAUST correspondence challenge where we achieve 20% (33%) improvement over state of the art methods for inter- (intra-) subject correspondence.

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