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Playable Video Generation
[article]
2021
arXiv
pre-print
This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled
arXiv:2101.12195v1
fatcat:rl2xllly2zb4ddhrbumt5elgrm