  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 as
inbetweening, and it belongs to the broader area of stochastic video
generation, which is generally approached by means of recurrent neural networks
(RNN). In this paper, we propose instead a fully convolutional model to
generate video sequences directly in the pixel domain. We first obtain a latent
video representation using a stochastic fusion mechanism that learns how to
incorporate information from the start and end frames. Our model learns to
produce such latent representation by progressively increasing the temporal
resolution, and then decode in the spatiotemporal domain using 3D convolutions.
The model is trained end-to-end by minimizing an adversarial loss. Experiments
on several widely-used benchmark datasets show that it is able to generate
meaningful and diverse in-between video sequences, according to both
quantitative and qualitative evaluations.
