  This presentation introduces a self-supervised learning approach to the
synthesis of new video clips from old ones, with several new key elements for
improved spatial resolution and realism: It conditions the synthesis process on
contextual information for temporal continuity and ancillary information for
fine control. The prediction model is doubly autoregressive, in the latent
space of an autoencoder for forecasting, and in image space for updating
contextual information, which is also used to enforce spatio-temporal
consistency through a learnable optical flow module. Adversarial training of
the autoencoder in the appearance and temporal domains is used to further
improve the realism of its output. A quantizer inserted between the encoder and
the transformer in charge of forecasting future frames in latent space (and its
inverse inserted between the transformer and the decoder) adds even more
flexibility by affording simple mechanisms for handling multimodal ancillary
information for controlling the synthesis process (eg, a few sample frames, an
audio track, a trajectory in image space) and taking into account the
intrinsically uncertain nature of the future by allowing multiple predictions.
Experiments with an implementation of the proposed approach give very good
qualitative and quantitative results on multiple tasks and standard benchmarks.
