Motion-Aware Gradient Domain Video Composition

by Tao Chen Jun-Yan Zhu Ariel Shamir Shi-Min Hu

Abstract

Gradient domain composition methods like Poisson blending offer practical solutions for uncertain object boundaries and differences in illumination conditions. However, adapting Poisson image blending to videos faces new challenges due to the addition of the temporal dimension. In videos, the human eye is sensitive to small changes in the blending boundaries across frames, and slight differences in the motion of the source patch and the target video. We present a novel video blending approach that tackles these problems by merging the gradient of source and target video and optimizing consistent blending boundary according to a user provided blending trimap for the source video. We extend the mean-value coordinates interpolation to support hybrid blending with dynamic boundary while maintaining interactive performance. We also provide a user interface and source object positioning method that can efficiently deal with complex video sequences beyond the capability of alpha blending.


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