Inverse texture synthesis

Abstract

Search-based texture synthesis algorithms are sensitive to the order in which texture samples are generated; different synthesis orders yield different textures. Unfortunately, most polygon rasterizers and ray tracers do not guarantee the order with which surfaces are sampled. To circumvent this problem, textures are synthesized beforehand at some maximum resolution and rendered using texture mapping.We describe a search-based texture synthesis algorithm in which samples can be generated in arbitrary order, yet the resulting texture remains identical. The key to our algorithm is a pyramidal representation in which each texture sample depends only on a fixed number of neighboring samples at each level of the pyramid. The bottom (coarsest) level of the pyramid consists of a noise image, which is small and predetermined. When a sample is requested by the renderer, all samples on which it depends are generated at once. Using this approach, samples can be generated in any order. To make the algorithm efficient, we propose storing texture samples and their dependents in a pyramidal cache. Although the first few samples are expensive to generate, there is substantial reuse, so subsequent samples cost less. Fortunately, most rendering algorithms exhibit good coherence, so cache reuse is high.

Image Retargeting by Texture-Aware Synthesis

“…For example-based texture synthesis, apparently the normal texture synthesis algorithms such as texture optimization [51] and appearance-space texture synthesis [52] are not fit for image retargeting since they are originally designed for enlargement but have no effective schemes for size decrease. Inverse texture synthesis (ITS) [38] can produce a small texture compaction that summarizes the original. Its framework is very similar as BDS method so it will suffer the same problems as BDS if being used for image retargeting.…”

“…On the other hand, common texture synthesis algorithms are designed for enlargement and can not be directly used for image retargeting applications. Wei [38] presents inverse texture synthesis approach to generate a smaller example from a large input texture. However, for globally varying textures, the output quality of this approach usually depends on the accuracy of the original map.…”

Reflectance modeling by neural texture synthesis

“…We address the orthogonal problem of solving for the SVBRDF in the first place. Wei et al [2008] propose a related unconventional use of texture synthesis. Their method reverses the usual procedure by producing a small texture exemplar that summarizes the content of a large (nonstationary) input image.…”

Local resampling for patch-based texture synthesis in vector fields

“…With these hardware-friendly improvements, their approach achieves real-time, and avoids blending effects in the results. Texture optimize has further been used for synthesizing solid texture from 2D texture exemplar [29], multiscale texture synthesis from multiple texture exemplars [30], and inverse texture synthesis [31].…”