Stable View Synthesis (by Vladlen Koltun from Intel)

Given a set of source images depicting a scene from arbitrary viewpoints, it synthesizes new views of the scene.

The method operates on a geometric scaffold computed via structure-from-motion and multi-view stereo. Each point on this 3D scaffold is associated with view rays and corresponding feature vectors that encode the appearance of this point in the input images. The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view. The target view is then rendered by a convolutional network from a tensor of features synthesized in this way for all pixels. The method is trained end-to-end.

The results are magnificent!

Source code
Paper