  We study how to synthesize novel views of human body from a single image.
Though recent deep learning based methods work well for rigid objects, they
often fail on objects with large articulation, like human bodies. The core step
of existing methods is to fit a map from the observable views to novel views by
CNNs; however, the rich articulation modes of human body make it rather
challenging for CNNs to memorize and interpolate the data well. To address the
problem, we propose a novel deep learning based pipeline that explicitly
estimates and leverages the geometry of the underlying human body. Our new
pipeline is a composition of a shape estimation network and an image generation
network, and at the interface a perspective transformation is applied to
generate a forward flow for pixel value transportation. Our design is able to
factor out the space of data variation and makes learning at each step much
easier. Empirically, we show that the performance for pose-varying objects can
be improved dramatically. Our method can also be applied on real data captured
by 3D sensors, and the flow generated by our methods can be used for generating
high quality results in higher resolution.
