1. Emotion in games.
de Melo, C., Paiva, A., & Gratch, J.
In M. Angelides, H. Agius (Eds.), The Handbook of Digital Games, 2014, (pp. 575-592), New Jersey: Wiley-IEEE Press, 2014
Growing interest on the study of emotion in the behavioral sciences has led to the development of several psychological theories of human emotion. These theories, in turn, inspired computer scientists to propose computational models that synthesize, express, recognize and interpret emotion. This cross-disciplinary research on emotion introduces new possibilities for digital games. Complementing techniques from the arts for drama and storytelling, these models can be used to drive believable non-player characters that experience properly-motivated emotions and express them appropriately at the right time; these theories can also help interpret the emotions the human player is experiencing and suggest adequate reactions in the game. This chapter reviews relevant psychological theories of emotion as well as computational models of emotion and discusses implications for games. We give special emphasis to appraisal theories of emotion, undeniably one of the most influential theoretical perspectives within computational research. In appraisal theories, emotions arise from cognitive appraisal of events (e.g., is this event conducive to my goals? Who is responsible for this event? Can I cope with this event?). According to the pattern of appraisals that occur, different emotions are experienced and expressed. Appraisal theories can, therefore, be used to synthesize emotions in games, which are then expressed in different ways. Complementary, reverse appraisal has been recently proposed as a theory for the interpretation of emotion. Accordingly, people are argued to retrieve, from emotion displays, information about how others' are appraising the ongoing interaction, which then leads to inferences about the others' intentions. Reverse appraisal can, thus, be used to infer how human players, from their emotion displays, are appraising the game experience and, from this information, what their intentions in the game are. This information can then be used to adjust game parameters or have non-player characters react to the player's intentions and, thus, contribute to improve the player's overall experience.
2. Modeling gesticulation expression in virtual humans.
de Melo, C., & Paiva, A.
In N. Magnenat-Thalmann, L. Jain, & N. Ichalkaranje (Eds.), New Advances in Virtual Humans, 2008, (pp. 133-151), Berlin Heidelberg: Springer-Verlag, 2008
Gesticulation is the kind of unconscious, idiosyncratic and unconventional gestures humans do in conversation or narration. This chapter reviews efforts made to harness the expressiveness of gesticulation in virtual humans and proposes one such model. First, psycholinguistics research is overviewed so as to understand how gesticulation occurs in humans. Then, relevant computer graphics and computational psycholinguistics systems are reviewed. Finally, a model for virtual human gesticulation expression is presented which supports: (a) real-time gesticulation animation described as sequences of constraints on static (Portuguese Sign Language hand shapes, orientation palm axis, orientation angle and handedness) and dynamic features; (b) synchronization between gesticulation and synthesized speech; (c) automatic reproduction of annotations in GestuRA, a gesticulation transcription algorithm; (d) expression control through an abstract integrated synchronized language Expression Markup Language (EML). Two studies, which were conducted to evaluate the model in a storytelling context, are also described.
3. Evolutionary expression of emotions in virtual humans using lights and pixels.
de Melo, C., & Paiva, A.
In J. Tao & T. Tan (Ed.), Affective Information Processing, 2008, (pp. 313-336), Springer Science+Business Media LLC, 2008
Artists express emotions through art. To accomplish this they rely on lines, shapes, textures, color, light, sounds, music, words and the body. The virtual humans field has been neglecting the kind of expression we see in the arts. In fact, researchers have tended to focus on gesture, face and voice for the expression of emotions. But why limit ourselves to the body? In this context, drawing on accumulated knowledge from the arts, this chapter describes an evolutionary model for the expression of emotions in virtual humans using lights, shadows, filters and composition. Lighting expression uses lighting techniques from the visual arts to convey emotions through the lights in the environment. Screen expression uses filters and composition to manipulate the virtual human's pixels themselves in a way akin to painting. Emotions are synthesized using the OCC model. To learn how to map affective states into lighting and screen expression, an evolutionary model which relies on genetic algorithms is used. The crossover and mutation operators generate alternatives for the expression of some affective state and a critic ensemble, composed of artificial and human critics, selects among the alternatives.
Last updated: September 6th, 2017