  In this paper, we propose a novel end-to-end architecture that could generate
a variety of plausible video sequences correlating two given discontinuous
frames. Our work is inspired by the human ability of inference. Specifically,
given two static images, human are capable of inferring what might happen in
between as well as present diverse versions of their inference. We firstly
train our model to learn the transformation to understand the movement trends
within given frames. For the sake of imitating the inference of human, we
introduce a latent variable sampled from Gaussian distribution. By means of
integrating different latent variables with learned transformation features,
the model could learn more various possible motion modes. Then applying these
motion modes on the original frame, we could acquire various corresponding
intermediate video sequence. Moreover, the framework is trained in adversarial
fashion with unsupervised learning. Evaluating on the moving Mnist dataset and
the 2D Shapes dataset, we show that our model is capable of imitating the human
inference to some extent.
