AppWarp: Retargeting Measured Materials by Appearance-Space Warping

We propose a method for retargeting measured materials, where a source measured material is edited by applying the reflectance functions of a template measured dataset. The resulting dataset is a material that maintains the spatial patterns of the source dataset, while exhibiting the reflectance behaviors of the template. Compared to editing materials by subsequent selections and modifications, retargeting shortens the time required to achieve a desired look by directly using template data, just as color transfer does for editing images. With our method, users have to just mark corresponding regions of source and template with rough strokes, with no need for further input. This paper introduces AppWarp, an algorithm that achieves retargeting as a user-constrained, appearance-space warping operation, that executes in tens of seconds. Our algorithm is independent of the measured material representation and supports retargeting of analytic and tabulated BRDFs as well as BSSRDFs. In addition, our method makes no assumption of the data distribution in appearance-space nor on the underlying correspondence between source and target. These characteristics make AppWarp the first general formulation for appearance retargeting. We validate our method on several types of materials, including leaves, metals, waxes, woods and greeting cards. Furthermore, we demonstrate how retargeting can be used to enhance diffuse texture with high quality reflectance.