  In this work, we focus on a challenging task: synthesizing multiple imaginary
videos given a single image. Major problems come from high dimensionality of
pixel space and the ambiguity of potential motions. To overcome those problems,
we propose a new framework that produce imaginary videos by transformation
generation. The generated transformations are applied to the original image in
a novel volumetric merge network to reconstruct frames in imaginary video.
Through sampling different latent variables, our method can output different
imaginary video samples. The framework is trained in an adversarial way with
unsupervised learning. For evaluation, we propose a new assessment metric
$RIQA$. In experiments, we test on 3 datasets varying from synthetic data to
natural scene. Our framework achieves promising performance in image quality
assessment. The visual inspection indicates that it can successfully generate
diverse five-frame videos in acceptable perceptual quality.
