AppWarp: Retargeting Measured Materials by Appearance-Space Warping

晃晃

AppWarp: Retargeting Measured Materials by Appearance-Space Warping

Xiaobo An   Xin Tongy Jonathan D. Denning Fabio Pellacini

Dartmouth College yMicrosoft Research Asia zSapienza University of Rome





Abstract

We propose a method for retargeting measured materials, where a

source measured material is edited by applying the reflectance func-

tions of a template measured dataset. The resulting dataset is a ma-

terial 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, re-

targeting 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 retar-

geting 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 formu-

lation for appearance retargeting. We validate our method on sev-

eral 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.



1 Introduction

Editing Measured Materials. In the past years, the use of

measured materials in Computer Graphics has grown since these

datasets capture the nuances of real-world surface appearance. For

many applications, editing these datasets is desired to permit artistic

control. The editing process is typically comprised of performing

soft selections on the data and applying edits to each selected re-

gion. For example, one might want to increase the roughness of the

body of a leaf without changing its stem. Prior work has focused on

simplifying selection [Pellacini and Lawrence 2007; An and Pel-

lacini 2008], but does not address the issue of finding the proper

editing parameters. For anything but the simplest cases, the latter

remains remarkably cumbersome since editing parameters are not

intuitively related to appearance changes. This implies that signifi-

cant trial-and-error is needed after selection, e.g., minutes for sim-

plest cases as reported in [Kerr and Pellacini 2010]. This is further

complicated by the fact that different material datasets use different

representations: from analytic (e.g. Cook-Torrance [1982] BRDFs)

to sampled tables (e.g., [Ashikhmin et al. 2000]). These different

representations require entirely different editing operations and po-

tentially hundreds of parameters for the sampled representations,

making the process even harder.



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tc

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晃晃

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奇

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奇

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tc

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