AppProp: all-pairs appearance-space edit propagation

Abstract

We present an intuitive and efficient method for editing the appearance of complex spatially-varying datasets, such as images and measured materials. In our framework, users specify rough adjustments that are refined interactively by enforcing the policy that similar edits are applied to spatially-close regions of similar appearance. Rather than proposing a specific user interface, our method allows artists to quickly and imprecisely specify the initial edits with any method or workflow they feel most comfortable with. An energy optimization formulation is used to propagate the initial rough adjustments to the final refined ones by enforcing the editing policy over all pairs of points in the dataset. We show that this formulation is equivalent to solving a large linear system defined by a dense matrix. We derive an approximate algorithm to compute such a solution interactively by taking advantage of the inherent structure of the matrix. We demonstrate our approach by editing images, HDR radiance maps, and measured materials. Finally, we show that our framework generalizes prior methods while providing significant improvements in generality, robustness and efficiency.