  Novel-View Human Action Synthesis aims to synthesize the movement of a body
from a virtual viewpoint, given a video from a real viewpoint. We present a
novel 3D reasoning to synthesize the target viewpoint. We first estimate the 3D
mesh of the target body and transfer the rough textures from the 2D images to
the mesh. As this transfer may generate sparse textures on the mesh due to
frame resolution or occlusions. We produce a semi-dense textured mesh by
propagating the transferred textures both locally, within local geodesic
neighborhoods, and globally, across symmetric semantic parts. Next, we
introduce a context-based generator to learn how to correct and complete the
residual appearance information. This allows the network to independently focus
on learning the foreground and background synthesis tasks. We validate the
proposed solution on the public NTU RGB+D dataset. The code and resources are
available at https://bit.ly/36u3h4K.
