Video to Video Synthesis
We study the problem of video to video synthesis, whose goal is to learn a mapping function from an input source video (e.g. a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image to image synthesis problem, is a popular topic, the video to video synthesis problem is less explored in the literature. Without understanding temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a novel video to video synthesis approach under the generative adversarial learning framework. Through carefully designed generator and discriminator architectures, coupled with a spatio temporal adversarial objective, we achieve high resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state of the art of video synthesis. Finally, we apply our approach to future video prediction, outperforming several state of the art competing systems.
NeurIPS