Domain-Agnostic Video Prediction from Motion Selective Kernels

Existing conditional video prediction approaches train a network from large databases and generalise to previously unseen data. We take the opposite stance, and introduce a model that learns from the first frames of a given video and extends its content and motion, to, e.g., double its length. To this end, we propose a dual network that can use in a flexible way both dynamic and static convolutional motion kernels, to predict future frames. We demonstrate experimentally the robustness of our approach on challenging videos in-the-wild and show that it is competitive w.r.t related baselines.