SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps.

Real-time graphics applications require high-quality textured materials to convey realism in virtual environments. Generating these textures is challenging as they need to be visually realistic, seamlessly tileable, and have a small impact on the memory consumption of the application. For this reason, they are often created manually by skilled artists.

Neural Photometry-Guided Visual Attribute Transfer.

We present a deep learning-based method for propagating spatially-varying visual material attributes (e.g., texture maps or image stylizations) to larger samples of the same or similar materials.

Color Control Functions for Multiprimary Displays I: Robustness Analysis and Optimization Formulations.

Color management for a multiprimary display requires, as a fundamental step, the determination of a color control function (CCF) that specifies control values for reproducing each color in the display's gamut. Multiprimary displays offer alternative choices of control values for reproducing a color in the interior of the gamut and accordingly alternative choices of CCFs. Under ideal conditions, alternative CCFs render colors identically.

Color Control Functions for Multiprimary Displays II: Variational Robustness Optimization.

In a companion Part I paper, we presented a framework for analyzing robustness of color control functions (CCFs) for multiprimary displays against primary and observer variations and proposed a variational minimization for obtaining robust CCFs. The objective function proposed in the Part I paper combines two nonnegative terms that serve as useful figures of merit for quantitatively characterizing CCFs. The first term measures lack of smoothness of the CCFs and characterizes how well transitions in perceptual color space are preserved in the presence of the primary/observer variations.

Decision tree learning to predict overweight/obesity based on body mass index and gene polymporphisms.

The new technologies for data analysis, such as decision tree learning, may help to predict the risk of developing diseases. The aim of the present work was to develop a pilot decision tree learning to predict overweight/obesity based on the combination of six single nucleotide polymorphisms (SNP) located in feeding-associated genes. Genotype study was performed in 151 healthy individuals, who were anonymized and randomly selected from the TALAVERA study.