Enabling Visual Composition and Animation in Unsupervised Video
  Generation
Aram Davtyan Sepehr Sameni Björn Ommer Paolo Favaro
In this work we propose a novel method for unsupervised controllable video
generation. Once trained on a dataset of unannotated videos, at inference our
model is capable of both composing scenes of predefined object parts and
animating them in a plausible and controlled way. This is achieved by
conditioning video generation on a randomly selected subset of local
pre-trained self-supervised features during training. We call our model CAGE
for visual Composition and Animation for video GEneration. We conduct a series
of experiments to demonstrate capabilities of CAGE in various settings. Project
website: https://araachie.github.io/cage.