  We present a transformation-grounded image generation network for novel 3D
view synthesis from a single image. Instead of taking a 'blank slate' approach,
we first explicitly infer the parts of the geometry visible both in the input
and novel views and then re-cast the remaining synthesis problem as image
completion. Specifically, we both predict a flow to move the pixels from the
input to the novel view along with a novel visibility map that helps deal with
occulsion/disocculsion. Next, conditioned on those intermediate results, we
hallucinate (infer) parts of the object invisible in the input image. In
addition to the new network structure, training with a combination of
adversarial and perceptual loss results in a reduction in common artifacts of
novel view synthesis such as distortions and holes, while successfully
generating high frequency details and preserving visual aspects of the input
image. We evaluate our approach on a wide range of synthetic and real examples.
Both qualitative and quantitative results show our method achieves
significantly better results compared to existing methods.
