Reviews: Learning to Decompose and Disentangle Representations for Video Prediction

The paper addresses the problem of predicting future frames in videos from previously seen frames. This task has been gaining a lot on popularity due to it's applications in different areas [8, 22], but also because it aims at modeling the underlying video representations in terms of motion and appearance. The paper proposes a new Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework that combines structured probabilistic models and deep networks to automatically (i) decompose the high-dimensional video that we aim to predict into components, and (ii) disentangle each component to have low-dimensional temporal dynamics that are easier to predict. The main novelty is the automatic decomposition of the video into components that are easier to predict, as well as the disentanglement of each component into static appearance and 2D motion. Positive sides: 1. Well written and easy to read.