Example-based texture synthesis on Disney's Tangled

Example-based texture synthesis on Disney's Tangled Example-based Texture Synthesis on Disney ™s Tangled Christian Eisenacher1 Chuck Tappan2 Brent Burley2 Daniel Teece2 1 2 University of Erlangen-Nuremberg Walt Disney Animation Studios Arthur Shek2 Figure 1: Left, displacement for scales, synthesized onto a chameleon with varying size. Middle, displacement for stones on a road, synthesis oriented by a vector ‚ow eld. Right, a texture to specify instancing sites for tile geometry, synthesized to arrange tiles organically on a roof. 1 Overview Look development on Walt Disney ™s animated feature Tangled called for artists to paint hundreds of organic elements with highresolution textures on a tight schedule. With our Ptex format [Burley and Lacewell 2008], we had the infrastructure to handle massive textures within our pipeline, but the task of manually painting the patterned textures would still involve tedious effort. We found that example-based texture synthesis, where an artist paints a small exemplar texture indicating a desired pattern that the system synthesizes over arbitrary surfaces, would alleviate some of the burden. In this talk we describe how we adapted existing synthesis methods to our Ptex-based work ‚ow, scaled them to productionsized textures, and addressed other engineering challenges. tage of Ptex ™s per-face granularity. We create the texture hierarchically from coarse to ne. For each level, we request each face and its adjacent faces from disk, perform a correction pass with proper ltering, and send the result back to disk. Synthesizing 600 million color texels requires 14 GB of temporary data and 30 min on an 8core machine. We are I/O bandwidth bound, as the complete texture needs to be streamed to/from disk for each iteration, but manage an average 7x multithreaded speedup with a two disk RAID 0. Work ‚ow Integration 2 Synthesis Details Our typical models use up to one giga-texel per surface, and complex models easily exceed that. However, few algorithms are even capable of synthesizing more than one mega-texel on a continuous surface. Our system is based on neighborhood matching, inspired by Lefebvre and Hoppe [2006], but addresses a number of challenges, including support for the texture sizes we require. We chose their method because it is inherently parallel, trades pre-processing for very fast synthesis, and has shown excellent synthesis quality over a wide range of inputs. Exemplar Analysis: Like Lefebvre and Hoppe, we pre-process the exemplar to speed up runtime synthesis. We create a set of box ltered images, extract neighborhoods, perform dimensionality reduction using PCA, and prune the search space using k-coherence. However, instead of determining the k most similar candidates for each neighborhood using a kd-tree, we pseudo-randomly sample the exemplar several times and keep the most similar neighborhood of 64, 16, and 4 samples. That way, we obtain k=3 suf ciently similar candidates with increasing randomness. This is considerably faster and allows signi cantly larger exemplars: up to 4096x4096 compared to the typical 64x64 or 128x128. The added variation turns out to be bene cial for synthesis quality with the hand painted exemplars we use. Our analysis is multi-threaded and a 1024x1024 exemplar is analyzed in about one minute on an 8-core machine. Synthesis: We cannot t all temporary data required to synthesize such large textures into main memory. Therefore, we directly operate on Ptex data and perform synthesis out-of-core, taking advanCopyright is held by the author / owner(s). SIGGRAPH 2010, Los Angeles, California, July 25 “ 29, 2010. ISBN 978-1-4503-0210-4/10/0007 Texture synthesis integrates nicely into our proprietary digital painting tool. Exposing it there complements existing methods for procedural texture generation via a custom shader expression language. Artists can conveniently browse from a shared library or paint an exemplar, and synthesize a texture across a surface with one click. The texture is immediately available for manual correction of problem areas. Our system allows artists to have ne control over varying feature size by either painting or procedurally generating a scale map, as done for the chameleon body in Fig. 1. Artists can also specify the orientation of the synthesized texture locally, using an interactively brushed vector ‚ow eld. It is represented as an oriented point cloud for easy interaction with external generators. Note the subtle interweaved curve-pattern of the stones on the road in Fig. 1. Conclusion and Future Work We have had great success synthesizing many organic and semiregular textures for Tangled. Artists enjoy the increased texture painting ef ciency and high level of art-directability. Next, we would like to explore ways to improve synthesis quality for very regular patterns like stone courses on irregular geometry, better integrate coarse and ne detail when using higher-res exemplars, and provide more intuitive tools for generating effective exemplars. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Example-based texture synthesis on Disney's Tangled

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The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
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10.1145/1837026.1837068
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Abstract

Example-based Texture Synthesis on Disney ™s Tangled Christian Eisenacher1 Chuck Tappan2 Brent Burley2 Daniel Teece2 1 2 University of Erlangen-Nuremberg Walt Disney Animation Studios Arthur Shek2 Figure 1: Left, displacement for scales, synthesized onto a chameleon with varying size. Middle, displacement for stones on a road, synthesis oriented by a vector ‚ow eld. Right, a texture to specify instancing sites for tile geometry, synthesized to arrange tiles organically on a roof. 1 Overview Look development on Walt Disney ™s animated feature Tangled called for artists to paint hundreds of organic elements with highresolution textures on a tight schedule. With our Ptex format [Burley and Lacewell 2008], we had the infrastructure to handle massive textures within our pipeline, but the task of manually painting the patterned textures would still involve tedious effort. We found that example-based texture synthesis, where an artist paints a small exemplar texture indicating a desired pattern that the system synthesizes over arbitrary surfaces, would alleviate some of the burden. In this talk we describe how we adapted existing synthesis methods to our Ptex-based work ‚ow, scaled them to productionsized textures, and addressed other engineering challenges. tage of Ptex ™s per-face granularity. We create the texture hierarchically from coarse to ne. For each level, we request each face and its adjacent faces from disk, perform a correction pass with proper ltering, and send the result back to disk. Synthesizing 600 million color texels requires 14 GB of temporary data and 30 min on an 8core machine. We are I/O bandwidth bound, as the complete texture needs to be streamed to/from disk for each iteration, but manage an average 7x multithreaded speedup with a two disk RAID 0. Work ‚ow Integration 2 Synthesis Details Our typical models use up to one giga-texel per surface, and complex models easily exceed that. However, few algorithms are even capable of synthesizing more than one mega-texel on a continuous surface. Our system is based on neighborhood matching, inspired by Lefebvre and Hoppe [2006], but addresses a number of challenges, including support for the texture sizes we require. We chose their method because it is inherently parallel, trades pre-processing for very fast synthesis, and has shown excellent synthesis quality over a wide range of inputs. Exemplar Analysis: Like Lefebvre and Hoppe, we pre-process the exemplar to speed up runtime synthesis. We create a set of box ltered images, extract neighborhoods, perform dimensionality reduction using PCA, and prune the search space using k-coherence. However, instead of determining the k most similar candidates for each neighborhood using a kd-tree, we pseudo-randomly sample the exemplar several times and keep the most similar neighborhood of 64, 16, and 4 samples. That way, we obtain k=3 suf ciently similar candidates with increasing randomness. This is considerably faster and allows signi cantly larger exemplars: up to 4096x4096 compared to the typical 64x64 or 128x128. The added variation turns out to be bene cial for synthesis quality with the hand painted exemplars we use. Our analysis is multi-threaded and a 1024x1024 exemplar is analyzed in about one minute on an 8-core machine. Synthesis: We cannot t all temporary data required to synthesize such large textures into main memory. Therefore, we directly operate on Ptex data and perform synthesis out-of-core, taking advanCopyright is held by the author / owner(s). SIGGRAPH 2010, Los Angeles, California, July 25 “ 29, 2010. ISBN 978-1-4503-0210-4/10/0007 Texture synthesis integrates nicely into our proprietary digital painting tool. Exposing it there complements existing methods for procedural texture generation via a custom shader expression language. Artists can conveniently browse from a shared library or paint an exemplar, and synthesize a texture across a surface with one click. The texture is immediately available for manual correction of problem areas. Our system allows artists to have ne control over varying feature size by either painting or procedurally generating a scale map, as done for the chameleon body in Fig. 1. Artists can also specify the orientation of the synthesized texture locally, using an interactively brushed vector ‚ow eld. It is represented as an oriented point cloud for easy interaction with external generators. Note the subtle interweaved curve-pattern of the stones on the road in Fig. 1. Conclusion and Future Work We have had great success synthesizing many organic and semiregular textures for Tangled. Artists enjoy the increased texture painting ef ciency and high level of art-directability. Next, we would like to explore ways to improve synthesis quality for very regular patterns like stone courses on irregular geometry, better integrate coarse and ne detail when using higher-res exemplars, and provide more intuitive tools for generating effective exemplars.

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