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What visual cues do human viewers use to assign personality characteristics to animated characters? While most facial animation systems associate facial actions to limited emotional states or speech content, the present paper explores the above question by relating the perception of personality to a wide variety of facial actions (e.g., head tilting/turning, and eyebrow raising) and emotional expressions (e.g., smiles and frowns). Animated characters exhibiting these actions and expressions were presented to human viewers in brief videos. Human viewers rated the personalities of these characters using a well-standardized adjective rating system borrowed from the psychological literature. These personality descriptors are organized in a multidimensional space that is based on the orthogonal dimensions of Desire for Affiliation and Displays of Social Dominance. The main result of the personality rating data was that human viewers associated individual facial actions and emotional expressions with specific personality characteristics very reliably. In particular, dynamic facial actions such as head tilting and gaze aversion tended to spread ratings along the Dominance dimension, whereas facial expressions of contempt and smiling tended to spread ratings along the Affiliation dimension. Furthermore, increasing the frequency and intensity of the head actions increased the perceived Social Dominance of the characters. We interpret these results as pointing to a reliable link between animated facial actions/expressions and the personality attributions they evoke in human viewers. The paper shows how these findings are used in our facial animation system to create perceptually valid personality profiles based on Dominance and Affiliation as two parameters that control the facial actions of autonomous animated characters.

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2 nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

We describe, in case study form, techniques to extend the range of facial types and movement using a parametric facial animation system originally developed to model and control synthetic 3D faces limited to a normal range of human shape and motion. These techniques have allowed us to create a single authoring system that can create and animate a wide range of facial types that range from realistic, stylized, cartoon-like, or a combination thereof, all from the same control system. Additionally we describe image processing and 3D deformation tools that allow for a greater range of facial types and facial animation output.