  We address the problem of reposing an image of a human into any desired novel
pose. This conditional image-generation task requires reasoning about the 3D
structure of the human, including self-occluded body parts. Most prior works
are either based on 2D representations or require fitting and manipulating an
explicit 3D body mesh. Based on the recent success in deep learning-based
volumetric representations, we propose to implicitly learn a dense feature
volume from human images, which lends itself to simple and intuitive
manipulation through explicit geometric warping. Once the latent feature volume
is warped according to the desired pose change, the volume is mapped back to
RGB space by a convolutional decoder. Our state-of-the-art results on the
DeepFashion and the iPER benchmarks indicate that dense volumetric human
representations are worth investigating in more detail.
