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Realistic characters from movies, games and simulations can make viewers feel strange (discomfort), an effect known as the Uncanny Valley (UV) theory. However, can the genders of CG characters and the genders of viewers change perceived comfort? In addition, can the genders (both characters and viewers) also influence the perceived charisma? Can the realism of a character also influence these aspects? This work aims to evaluate the perception of women and men about female and male characters, created using Computer Graphics (CG), presented in various media (movies, games, computer simulations, among others). Our goal is to answer the following questions: (i) How does the comfort perceived by people of both tested genders (female and male) relate to the genders of the characters? and (ii) Is the charisma influenced by the realism of the characters, considering the subjects and genders of the characters? We conducted perceptual studies on characters created using CG in images and videos through questionnaires. Our results indicated that the gender of the subjects and characters affected comfort, charisma and perceived realism. In addition, we also revisited the aspect of the UV theory (perception of comfort and human likeness) and found coherent curves compared to many works in the literature. KeywordsUncanny valley • Perceived comfort • Perceived realism • Perceived charisma • Cg characters • Gender

With the popularization of social networks, the sharing and consumption of content in video format becomes easier. Understanding what makes a video popular and being able to predict its popularity in number of views is useful for both content creators and advertising. In this work, we explore visual features extracted from 1,820 Facebook videos in order to predict whether they will reach more than a certain number of views on the seven days after publication. For this purpose, we used Support Vector Machine with Gaussian Radial Basis Function classification model. Using only visual features as predictors, the model with Video Characteristics and Rigidity features combined reached Kappa of 0.7324, sensitivity of 0.8930, and positive predictive value of 0.8930.

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The sense of strangeness (or discomfort) perceived in certain virtual characters, discussed in Uncanny Valey (UV) theory, can be a key factor in our perceptual and cognitive discrimination. Understanding how this strangeness happens is essential to avoid it in the process of modeling virtual humans. In this paper, we investigate the relationship between images features and the discomfort that human beings can perceive. We extract image features based on Hu Moments (Hum) and Histogram Oriented Gradient (Hog). The saliency detection is also extracted in the specific parts of the virtual face. Finally, a model using Support Vector Machine (SVM) to provide binary classification is suggested. The results indicate accuracy of around 80% in the image estimation process comparing with subjective classification. As a contribution, some areas may benefit from this study for avoiding the creation of characters that may cause strangeness, such as the games, conversational agents and cinema industry.