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We consider the problem of generating plausible and diverse video sequences,when we are only given a start and an end frame. This task is also known asinbetweening, and it belongs to the broader area of stochastic videogeneration, which is generally approached by means of recurrent neural networks(RNN). In this paper, we propose instead a fully convolutional model togenerate video sequences directly in the pixel domain. We first obtain a latentvideo representation using a stochastic fusion mechanism that learns how toincorporate information from the start and end frames. Our model learns toproduce such latent representation by progressively increasing the temporalresolution, and then decode in the spatiotemporal domain using 3D convolutions.The model is trained end-to-end by minimizing an adversarial loss. Experimentson several widely-used benchmark datasets show that it is able to generatemeaningful and diverse in-between video sequences, according to bothquantitative and qualitative evaluations.

From Here to There: Video Inbetweening Using Direct 3D Convolutions