Human body movements and postures carry emotion-specific information. On the basis of this motivation, the objective of this study is to analyze this information in the spatial and temporal structure of the motion capture data and extract features that are indicative of certain emotions in terms of affective state descriptors. Our contribution comprises identifying the directly or indirectly related descriptors to emotion classification in human motion and conducting a comprehensive analysis of these descriptors (features) that fall into three different categories: posture descriptors, dynamic descriptors, and frequency-based descriptors in order to measure their performance with respect to predicting the affective state of an input motion. The classification results demonstrate that no single category is sufficient by itself; the best prediction performance is achieved when all categories are combined.

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Our paper introduces a novel approach for controlling stereo camera parameters in interactive 3D environments in a way that specifically addresses the interplay of binocular depth perception and saliency of scene contents. Our proposed Dynamic Attention-Aware Disparity Control (DADC) method produces depth-rich stereo rendering that improves viewer comfort through joint optimization of stereo parameters. While constructing the optimization model, we consider the importance of scene elements, as well as their distance to the camera and the locus of attention on the display. Our method also optimizes the depth effect of a given scene by considering the individual user's stereoscopic disparity range and comfortable viewing experience by controlling accommodation/convergence conflict. We validate our method in a formal user study that also reveals the advantages, such as superior quality and practical relevance, of considering our method.

Caricature is an illustration of a person or a subject that uses a way of exaggerating the most distinguishable characteristic traits and simplifying the common features in order to magnify the unique features of the subject. Recently, automatic caricature generation has become a research area due to the advantageous features of amusement in the fields such as network, communications, online games, and the animation industry. The aim of this study is to present a perceptual caricaturization approach practicing the concept of exaggeration, which is very common in traditional art and caricature, on 3D mesh models synthesizing the idea of mesh saliency.

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