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.
CITATION STYLE
Jin, S., Liu, R., Ji, Y., Ye, J., & Yu, J. (2018). Learning to dodge a bullet: Concyclic view morphing via deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11218 LNCS, pp. 230–246). Springer Verlag. https://doi.org/10.1007/978-3-030-01264-9_14
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