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Video prediction aims to generate realistic future frames by learning dynamicvisual patterns. One fundamental challenge is to deal with future uncertainty:How should a model behave when there are multiple correct, equally probablefuture? We propose an Appearance-Motion Conditional GAN to address thischallenge. We provide appearance and motion information as conditions thatspecify how the future may look like, reducing the level of uncertainty. Ourmodel consists of a generator, two discriminators taking charge of appearanceand motion pathways, and a perceptual ranking module that encourages videos ofsimilar conditions to look similar. To train our model, we develop a novelconditioning scheme that consists of different combinations of appearance andmotion conditions. We evaluate our model using facial expression and humanaction datasets and report favorable results compared to existing methods.

Video Prediction with Appearance and Motion Conditions