Learning to Dodge A Bullet: Concyclic View Morphing via Deep Learning

Abstract

The bullet-time effect, presented in feature film ``The Matrix", has been widely adopted in feature films and TV commercials to create an amazing stopping-time illusion. Producing such visual effects, however, typically requires using a large number of cameras/images surrounding the subject. In this paper, we present a learning-based solution that is capable of producing the bullet-time effect from only a small set of images. Specifically, we present a view morphing framework that can synthesize smooth and realistic transitions along extit{a circular view path} using as few as three reference images. We apply a novel cyclic rectification technique to align the reference images onto a common circle and then feed the rectified results into a deep network to predict its motion field and per-pixel visibility for new view interpolation. Comprehensive experiments on synthetic and real data show that our new framework outperforms the state-of-the-art and provides an inexpensive and practical solution for producing the bullet-time effects.