Machine Learning for Video-Based Rendering

This work extends the new paradigm for computer animation, video textures, whichuses recorded video to generate novel animations by replaying the video samples in a new order. Here we concentrate on video sprites, which are a special type of video texture. In video sprites, instead of storing whole images, the object of interest isseparated from the background and the video samples are stored as a sequence of alpha-matted sprites with associated velocity information.They can be rendered anywhere on the screen to create a novel animation of the object. We present methods to create suchanimations by finding a sequence of sprite samples that is both visually smooth and follows a desired path. To estimate visual smoothness, we train a linear classifier to estimate visual similarity between video samples.