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Given the three dimensional complexity of a video signal, training a robustand diverse GAN based video generative model is onerous due to largestochasticity involved in data space. Learning disentangled representations ofthe data help to improve robustness and provide control in the samplingprocess. For video generation, there is a recent progress in this area byconsidering motion and appearance as orthogonal information and designingarchitectures that efficiently disentangle them. These approaches rely onhandcrafting architectures that impose structural priors on the generator todecompose appearance and motion codes in the latent space. Inspired from therecent advancements in the autoencoder based image generation, we present AVLAE(Adversarial Video Latent AutoEncoder) which is a two stream latent autoencoderwhere the video distribution is learned by adversarial training. In particular,we propose to autoencode the motion and appearance latent vectors of the videogenerator in the adversarial setting. We demonstrate that our approach learnsto disentangle motion and appearance codes even without the explicit structuralcomposition in the generator. Several experiments with qualitative andquantitative results demonstrate the effectiveness of our method.

Autoencoding Video Latents for Adversarial Video Generation