  We present an approach that learns to synthesize high-quality, novel views of
3D objects or scenes, while providing fine-grained and precise control over the
6-DOF viewpoint. The approach is self-supervised and only requires 2D images
and associated view transforms for training. Our main contribution is a network
architecture that leverages a transforming auto-encoder in combination with a
depth-guided warping procedure to predict geometrically accurate unseen views.
Leveraging geometric constraints renders direct supervision via depth or flow
maps unnecessary. If large parts of the object are occluded in the source view,
a purely learning based prior is used to predict the values for dis-occluded
pixels. Our network furthermore predicts a per-pixel mask, used to fuse
depth-guided and pixel-based predictions. The resulting images reflect the
desired 6-DOF transformation and details are preserved. We thoroughly evaluate
our architecture on synthetic and real scenes and under fine-grained and
fixed-view settings. Finally, we demonstrate that the approach generalizes to
entirely unseen images such as product images downloaded from the internet.
