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 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