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We introduce a new encoder-decoder GAN model, FutureGAN, that predicts futureframes of a video sequence conditioned on a sequence of past frames. Duringtraining, the networks solely receive the raw pixel values as an input, withoutrelying on additional constraints or dataset specific conditions. To captureboth the spatial and temporal components of a video sequence, spatio-temporal3d convolutions are used in all encoder and decoder modules. Further, weutilize concepts of the existing progressively growing GAN (PGGAN) thatachieves high-quality results on generating high-resolution single images. TheFutureGAN model extends this concept to the complex task of video prediction.We conducted experiments on three different datasets, MovingMNIST, KTH Action,and Cityscapes. Our results show that the model learned representations totransform the information of an input sequence into a plausible future sequenceeffectively for all three datasets. The main advantage of the FutureGANframework is that it is applicable to various different datasets withoutadditional changes, whilst achieving stable results that are competitive to thestate-of-the-art in video prediction. Our code is available athttps://github.com/TUM-LMF/FutureGAN.

FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs