Transfusive Weights for Content-Aware Image Manipulation

In this paper, we expand the range of applications for content-aware weights to the multi-image setting and improve the quality of the recently proposed weights and the matching framework.

Many image editing operations, such as colorization, matting or deformation, can be performed by propagating user-defined sparse constraints (e.g. scribbles) to the rest of the image using content-aware weight functions. Image manipulation has been recently extended to simultaneous editing of multiple images of the same subject or scene by precomputing dense correspondences, where the content-aware weights play a core role in defining the sub-pixel accurate image warps from source to target images. In this paper, we expand the range of applications for content-aware weights to the multi-image setting and improve the quality of the recently proposed weights and the matching framework. We show that multiple images of a subject can be used to refine the content-aware weights, and we propose a customization of the weights to enable easily-controllable interactive depth segmentation and assignment, image matting and deformation transfer, both in single- and multi-image settings.