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This paper proposes the novel task of video generation conditioned on aSINGLE semantic label map, which provides a good balance between flexibilityand quality in the generation process. Different from typical end-to-endapproaches, which model both scene content and dynamics in a single step, wepropose to decompose this difficult task into two sub-problems. As currentimage generation methods do better than video generation in terms of detail, wesynthesize high quality content by only generating the first frame. Then weanimate the scene based on its semantic meaning to obtain the temporallycoherent video, giving us excellent results overall. We employ a cVAE forpredicting optical flow as a beneficial intermediate step to generate a videosequence conditioned on the initial single frame. A semantic label map isintegrated into the flow prediction module to achieve major improvements in theimage-to-video generation process. Extensive experiments on the Cityscapesdataset show that our method outperforms all competing methods.

Video Generation from Single Semantic Label Map