Regenerative Morphing

We present a new image morphing approach in which the output sequence is regenerated from small pieces of the two source (input) images. The approach does not require manual correspondence, and generates compelling results even when the images are of very different objects (e.g., a cloud and a face). We pose the morphing task as an optimization with the objective of achieving bidirectional similarity of each frame to its neighbors, and also to the source images. The advantages of this approach are 1) it can operate fully automatically, producing effective results for many sequences (but also supports manual correspondences, when available), 2) ghosting artifacts are minimized, and 3) different parts of the scene move at different rates, yielding more interesting (and less robotic) transitions.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Eli Shechtman, Steve Seitz, Alex Rav-Acha, Michal Irani
• Keywords: Computer Vision, CVPR 2010, Image Morphing Approach, Manual Correspondences, Output Sequence