  Prediction and interpolation for long-range video data involves the complex
task of modeling motion trajectories for each visible object, occlusions and
dis-occlusions, as well as appearance changes due to viewpoint and lighting.
Optical flow based techniques generalize but are suitable only for short
temporal ranges. Many methods opt to project the video frames to a low
dimensional latent space, achieving long-range predictions. However, these
latent representations are often non-interpretable, and therefore difficult to
manipulate. This work poses video prediction and interpolation as unsupervised
latent structure inference followed by a temporal prediction in this latent
space. The latent representations capture foreground semantics without explicit
supervision such as keypoints or poses. Further, as each landmark can be mapped
to a coordinate indicating where a semantic part is positioned, we can reliably
interpolate within the coordinate domain to achieve predictable motion
interpolation. Given an image decoder capable of mapping these landmarks back
to the image domain, we are able to achieve high-quality long-range video
interpolation and extrapolation by operating on the landmark representation
space.
