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The ability to estimate the 3D human shape and pose from images can be usefulin many contexts. Recent approaches have explored using graph convolutionalnetworks and achieved promising results. The fact that the 3D shape isrepresented by a mesh, an undirected graph, makes graph convolutional networksa natural fit for this problem. However, graph convolutional networks havelimited representation power. Information from nodes in the graph is passed toconnected neighbors, and propagation of information requires successive graphconvolutions. To overcome this limitation, we propose a dual-scale graphapproach. We use a coarse graph, derived from a dense graph, to estimate thehuman's 3D pose, and the dense graph to estimate the 3D shape. Information incoarse graphs can be propagated over longer distances compared to dense graphs.In addition, information about pose can guide to recover local shape detail andvice versa. We recognize that the connection between coarse and dense is itselfa graph, and introduce graph fusion blocks to exchange information betweengraphs with different scales. We train our model end-to-end and show that wecan achieve state-of-the-art results for several evaluation datasets.

Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph