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.
@cite_19: Despite the crucial role of physical appearance in forming first impressions, little research has examined the accuracy of personality impressions based on appearance alone. This study examined the accuracy of observers’ impressions on 10 personality traits based on full-body photographs using criterion measures based on self and peer reports. When targets’ posture and expression were constrained (standardized condition), observers’ judgments were accurate for extraversion, self-esteem, and religiosity. When targets were photographed with a spontaneous pose and facial expression (spontaneous condition), observers’ judgments were accurate for almost all of the traits examined. Lens model analyses demonstrated that both static cues (e.g., clothing style) and dynamic cues (e.g., facial expression, posture) offered valuable personality-relevant information. These results suggest that personality is manifested through both static and expressive channels of appearance, and observers use this information to form...
@cite_10: First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable “ambient” face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58 of the variance in raters’ impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
Personality impression is an active research topic in psychology and cognitive science. Researchers are interested in studying how different factors, , face, body, profile, motion, influence the formation of personality impression on others @cite_19 . Recent work @cite_10 suggests that facial appearances play an important role in giving personality impressions.