Generative comics: a character evolution approach for creating fictional comics

Comics can be a suitable form of representation for generative narrative. This paper provides an argument for this based on an analysis of properties of the comics medium, and describes a tool for character design and comic strip creation that applies interactive evolution methods to characters in a virtual environment. The system is used to interactively create artificial characters with extreme personality traits inspired by well-known comics characters

Exporting Vector Muscles for Facial Animation

In this paper we introduce a method of exporting vector muscles from one 3D face to another for facial animation. Starting from a 3D face with an extended version of Waters' linear muscle system, we transfer the linear muscles to a target 3D face. We also transfer the region division, which is used to increase the performance of the muscle as well as to control the animation. The human involvement is just as simple as selecting the faces which shows the most natural facial expressions in the animator's view. The method allows the transfer of the animation to a new 3D model within a short time. The transferred muscles can then be used to create new animations

Facial Expression Retargeting from Human to Avatar Made Easy

Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies on the modelers' experience. In this paper, we propose a brand-new solution to this cross-domain expression transfer problem via nonlinear expression embedding and expression domain translation. We first build low-dimensional latent spaces for the human and avatar facial expressions with variational autoencoder. Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints. Specifically, we design geometric correspondences to reflect geometric matching and utilize a triplet data structure to express users' perceptual preference of avatar expressions. A user-friendly method is proposed to automatically generate triplets for a system allowing users to easily and efficiently annotate the correspondences. Using both geometric and perceptual correspondences, we trained a network for expression domain translation from human to avatar. Extensive experimental results and user studies demonstrate that even nonprofessional users can apply our method to generate high-quality facial expression retargeting results with less time and effort.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Using genetic algorithms to uncover individual differences in how humans represent facial emotion

Emotional facial expressions critically impact social interactions and cognition. However, emotion research to date has generally relied on the assumption that people represent categorical emotions in the same way, using standardized stimulus sets and overlooking important individual differences. To resolve this problem, we developed and tested a task using genetic algorithms to derive assumption-free, participant-generated emotional expressions. One hundred and five participants generated a subjective representation of happy, angry, fearful and sad faces. Population-level consistency was observed for happy faces, but fearful and sad faces showed a high degree of variability. High test-retest reliability was observed across all emotions. A separate group of 108 individuals accurately identified happy and angry faces from the first study, while fearful and sad faces were commonly misidentified. These findings are an important first step towards understanding individual differences in emotion representation, with the potential to reconceptualize the way we study atypical emotion processing in future research

Development of the huggable social robot Probo: on the conceptual design and software architecture

This dissertation presents the development of a huggable social robot named Probo. Probo embodies a stuffed imaginary animal, providing a soft touch and a huggable appearance. Probo's purpose is to serve as a multidisciplinary research platform for human-robot interaction focused on children. In terms of a social robot, Probo is classified as a social interface supporting non-verbal communication. Probo's social skills are thereby limited to a reactive level. To close the gap with higher levels of interaction, an innovative system for shared control with a human operator is introduced. The software architecture de nes a modular structure to incorporate all systems into a single control center. This control center is accompanied with a 3D virtual model of Probo, simulating all motions of the robot and providing a visual feedback to the operator. Additionally, the model allows us to advance on user-testing and evaluation of newly designed systems. The robot reacts on basic input stimuli that it perceives during interaction. The input stimuli, that can be referred to as low-level perceptions, are derived from vision analysis, audio analysis, touch analysis and object identification. The stimuli will influence the attention and homeostatic system, used to de ne the robot's point of attention, current emotional state and corresponding facial expression. The recognition of these facial expressions has been evaluated in various user-studies. To evaluate the collaboration of the software components, a social interactive game for children, Probogotchi, has been developed. To facilitate interaction with children, Probo has an identity and corresponding history. Safety is ensured through Probo's soft embodiment and intrinsic safe actuation systems. To convey the illusion of life in a robotic creature, tools for the creation and management of motion sequences are put into the hands of the operator. All motions generated from operator triggered systems are combined with the motions originating from the autonomous reactive systems. The resulting motion is subsequently smoothened and transmitted to the actuation systems. With future applications to come, Probo is an ideal platform to create a friendly companion for hospitalised children

Emotions, behaviour and belief regulation in an intelligent guide with attitude

Abstract unavailable please refer to PD

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion