Regenerative morphing
Regenerative morphing
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DOI：
10.1109/cvpr.2010.5540159
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Eli Shechtman;A. Rav-Acha;M. Irani;S. Seitz
中科院分区：
文献类型：
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作者：
Eli Shechtman;A. Rav-Acha;M. Irani;S. Seitz
我们提出了一种新的图像变形方法，其中输出序列是由两个源（输入）图像的小块重新生成的。该方法不需要手动通信，即使图像是非常不同的对象（例如，云和人脸），也会产生令人信服的结果。我们将变形任务作为一种优化，其目标是实现每帧与其相邻图像以及源图像的双向相似性。这种方法的优点是1)它可以完全自动操作，为许多序列产生有效的结果（但也支持手动对应，当可用时），2)重影伪影被最小化，3)场景的不同部分以不同的速率移动，产生更有趣的（和更少的机器人）过渡。
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.