3D Face Synthesis Driven by Personality Impression

Synthesizing 3D faces that give certain personality impressions is commonly needed in computer games, animations, and virtual world applications for producing realistic virtual characters. In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters. Our approach consists of two major steps. In the first step, we train classifiers using deep convolutional neural networks on a dataset of images with personality impression annotations, which are capable of predicting the personality impression of a face. In the second step, given a 3D face and a desired personality impression type as user inputs, our approach optimizes the facial details against the trained classifiers, so as to synthesize a face which gives the desired personality impression. We demonstrate our approach for synthesizing 3D faces giving desired personality impressions on a variety of 3D face models. Perceptual studies show that the perceived personality impressions of the synthesized faces agree with the target personality impressions specified for synthesizing the faces. Please refer to the supplementary materials for all results.Comment: 8pages;6 figure

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Strength and Direction of Demographic Variables as Determinants of Driver-Chauffeur Satisfaction in Judicial Procedural Fairness

Studies have shown that treating litigants fairly is one way by which public confidence can be improved and sustained in the judiciary. Demographic features of such individuals may also influence their confidence indirectly and how they are satisfied with the procedures in the criminal justice system directly. The rationale of implementing the study was therefore to establish the extent of dissatisfaction of procedural fairness within the driver-chauffeur community in Cape Coast Metropolis in the Central Region of Ghana. The objectives were to determine the strength, direction and odds ratio of the major satisfaction predictors with reference to demographic characteristics. The study employed the purposive method, dwelling on the accidental technique to sample drivers and chauffeurs who had on one occasion or more fallen victim of traffic offence. Eighty subjects were sampled. The demographic variables considered include age; driving experience; type of vehicle; brand of vehicle; religion; and highest education attainment in relation to ten judicial fairness characteristics. Logistic regression was employed as the statistical tool to analyze the data using Statistical Package for the Social Sciences (SPSS) software, version 21. Generally, factors that influenced satisfaction were experience in driving (β2 = -0.667); type of vehicle (β3 = 0.553); religion (β4 = -0.363); highest educational attainment (β5 = -1.306) and brand of vehicle (β6 = -1.809). The contribution of age to the combined model was insignificant (ρ = 0.337). The paper recommends that judges in the metropolis should take these factors into consideration when hearing and deciding on matters concerning traffic offences.Key words: age; driving experience; educational attainment; religion; vehicle brand; vehicle type

WorldBrush: Interactive Example-based Synthesis of Procedural Virtual Worlds

International audienceWe present a novel approach for the interactive synthesis and editing of virtual worlds. Our method is inspired by painting operations and uses methods for statistical example-based synthesis to automate content synthesis and deformation. Our real-time approach takes a form of local inverse procedural modeling based on intermediate statistical models: selected regions of procedurally and manually constructed example scenes are analyzed, and their parameters are stored as distributions in a palette, similar to colors on a painter’s palette. These distributions can then be interactively applied with brushes and combined in various ways, like in painting systems. Selected regions can also be moved or stretched while maintaining the consistency of their content. Our method captures both distributions of elements and structured objects, and models their interactions. Results range from the interactive editing of 2D artwork maps to the design of 3D virtual worlds, where constraints set by the terrain’s slope are also taken into account