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In this paper, we propose a novel end-to-end architecture that could generatea variety of plausible video sequences correlating two given discontinuousframes. Our work is inspired by the human ability of inference. Specifically,given two static images, human are capable of inferring what might happen inbetween as well as present diverse versions of their inference. We firstlytrain our model to learn the transformation to understand the movement trendswithin given frames. For the sake of imitating the inference of human, weintroduce a latent variable sampled from Gaussian distribution. By means ofintegrating different latent variables with learned transformation features,the model could learn more various possible motion modes. Then applying thesemotion modes on the original frame, we could acquire various correspondingintermediate video sequence. Moreover, the framework is trained in adversarialfashion with unsupervised learning. Evaluating on the moving Mnist dataset andthe 2D Shapes dataset, we show that our model is capable of imitating the humaninference to some extent.

Brain-Inspired Inference on Missing Video Sequence