Transfusive Weights for Content-Aware Image Manipulation

Abstract

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.