title: Statistical Invariance for Texture Synthesis.
abstract: Estimating illumination and deformation fields on textures is essential for both analysis and application purposes. Traditional methods for such estimation usually require complicated and sometimes labor-intensive processing. In this paper, we propose a new perspective for this problem and suggest a novel statistical approach which is much simpler and more efficient. Our experiments show that many textures in daily life are statistically invariant in terms of colors and gradients. Variations of such statistics can be assumed to be influenced by illumination and deformation. This implies that we can inversely estimate the spatially varying illumination and deformation according to the variation of the texture statistics. This enables us to decompose a texture photo into an illumination field, a deformation field, and an implicit texture which are illumination- and deformation-free, within a short period of time, and with minimal user input. By processing and recombining these components, a variety of synthesis effects, such as exemplar preparation, texture replacement, surface relighting, as well as geometry modification, can be well achieved. Finally, convincing results are shown to demonstrate the effectiveness of the proposed method.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Block N4-B1b-13, North Spine, 50 Nanyang Avenue, Singapore 639798.  aurorean.xp@gmail.com
authors.forename: Xiaopei
authors.initials: X
authors.lastname: Liu
authors.forename: Lei
authors.initials: L
authors.lastname: Jiang
authors.forename: Tien-Tsin
authors.initials: TT
authors.lastname: Wong
authors.forename: Chi-Wing
authors.initials: CW
authors.lastname: Fu