Transfusive Image Manipulation

Project Members

Kaan Yücer (Disney Research Zurich)
Alec Jacobson (ETH Zurich)
Alexander Sorkine-Hornung (Disney Research Zurich)
Olga Sorkine-Hornung (ETH Zurich)


In this example, we edited the upper left image and added ivy on the wall, additional windows, and cracks in the building (bottom left). These edits are then automatically propagated to a collection of images of the same object taken from different perspectives and orientations.


We present a method for consistent automatic transfer of edits applied to one image to many other images of the same object or scene.  By introducing novel, content-adaptive weight functions we enhance the non-rigid alignment framework of Lucas-Kanade to robustly handle changes of viewpoint, illumination and non-rigid deformations of the subjects.  Our weight functions are content-aware and possess high-order smoothness, enabling to define high-quality image warping with a low number of parameters using spatially-varying weighted combinations of affine deformations. Optimizing the warp parameters leads to subpixel-accurate alignment while maintaining computation efficiency.  Our method allows users to perform precise, localized edits such as simultaneous painting on multiple images in real-time, relieving them from tedious and repetitive manual reapplication to each individual image.


Transfusive Weights for Content-Aware Image Manipulation-Thumbnail

Transfusive Weights for Content-Aware Image Manipulation
September 11, 2013
International Workshop on Vision Modeling and Visualization (VMV) 2013
Paper File [pdf, 8.55 MB]

Tranfusive Image Manipulation-Thumbnail1

Transfusive Image Manipulation
November 30, 2012
Paper File [pdf, 19.30 MB]

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