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

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-01264-9_14

1611-3349

Jin Shi, Ruiynag Liu, Yu Ji, Jinwei Ye, Jingyi Yu,

Computer Graphics and Visualization Techniques

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.