Dual Mesh Convolutional Networks for Human Shape Correspondence


Nitika Verma, Adnane Boukhayma, Edmond Boyer and Jakob Verbeek


Convolutional networks have been extremely successful for regular data structures such as 2D images and 3D voxel grids. The transposition to meshes is, however, not straightforward due to their irregular structure. We explore how the dual, face-based representation of triangular meshes can be leveraged as a data structure for graph convolutional networks. In the dual mesh, each node (face) has a fixed number of neighbors, which makes the networks less susceptible to overfitting on the mesh topology, and also allows the use of input features that are naturally defined over faces, such as surface normals and face areas. We evaluate the dual approach on the shape correspondence task on the Faust human shape dataset and variants of it with different mesh topologies. Our experiments show that results of graph convolutional networks improve when defined over the dual rather than primal mesh. Moreover, our models that explicitly leverage the neighborhood regularity of dual meshes allow improving results further while being more robust to changes in the mesh topology.

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  Important Dates

All deadlines are 23:59 Pacific Time (PT). No extensions will be granted.

Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021