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