Avatar Facial Expressions Research Articles

Facial expression modifies female body perception.

The judgment of female body appearance has been reported to be affected by a range of internal (e.g., viewers' sexual cognition) and external factors (e.g., viewed clothing type and colour). This eye-tracking study aimed to complement previous research by examining the effect of facial expression on female body perception and associated body-viewing gaze behaviour. We presented female body images of Caucasian avatars in a continuum of common dress sizes posing seven basic facial expressions (neutral, happiness, sadness, anger, fear, surprise, and disgust), and asked both male and female participants to rate the perceived body attractiveness and body size. The analysis revealed an evident modulatory role of avatar facial expressions on body attractiveness and body size ratings, but not on the amount of viewing time directed at individual body features. Specifically, happy and angry avatars attracted the highest and lowest body attractiveness ratings, respectively, and fearful and surprised avatars tended to be rated slimmer. Interestingly, the impact of facial expression on female body assessment was not further influenced by viewers' gender, suggesting a 'universal' role of common facial expressions in modifying the perception of female body appearance.

The effect of avatar facial expressions on trust building in social virtual reality

The effect of avatar facial expressions on trust building in social virtual reality

Temporal lobe epilepsy alters neural responses to human and avatar facial expressions in the face perception network.

Background and ObjectiveAlthough avatars are now widely used in advertisement, entertainment, and business today, no study has investigated whether brain lesions in neurological patients interfere with brain activation in response to dynamic avatar facial expressions. The aim of our event‐related fMRI study was to compare brain activation differences in people with epilepsy and controls during the processing of fearful and neutral dynamic expressions displayed by human or avatar faces.MethodsUsing functional magnetic resonance imaging (fMRI), we examined brain responses to dynamic facial expressions of trained actors and their avatar look‐alikes in 16 people with temporal lobe epilepsy (TLE) and 26 controls. The actors' fearful and neutral expressions were recorded on video and conveyed onto their avatar look‐alikes by face tracking.ResultsOur fMRI results show that people with TLE exhibited reduced response differences between fearful and neutral expressions displayed by humans in the right amygdala and the left superior temporal sulcus (STS). Further, TLE was associated with reduced response differences between human and avatar fearful expressions in the dorsal pathway of the face perception network (STS and inferior frontal gyrus) as well as in the medial prefrontal cortex.ConclusionsTaken together, these findings suggest that brain responses to dynamic facial expressions are altered in people with TLE compared to neurologically healthy individuals—regardless of whether the face is human or computer‐generated. In TLE, areas sensitive to dynamic facial features and associated with processes relating to the self and others are particularly affected when processing dynamic human and avatar expressions. Our findings highlight that the impact of TLE on facial emotion processing must be extended to artificial faces and should be considered when applying dynamic avatars in the context of neurological conditions.

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 article, 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.

Dynamic human and avatar facial expressions elicit differential brain responses.

Computer-generated characters, so-called avatars, are widely used in advertising, entertainment, human–computer interaction or as research tools to investigate human emotion perception. However, brain responses to avatar and human faces have scarcely been studied to date. As such, it remains unclear whether dynamic facial expressions of avatars evoke different brain responses than dynamic facial expressions of humans. In this study, we designed anthropomorphic avatars animated with motion tracking and tested whether the human brain processes fearful and neutral expressions in human and avatar faces differently. Our fMRI results showed that fearful human expressions evoked stronger responses than fearful avatar expressions in the ventral anterior and posterior cingulate gyrus, the anterior insula, the anterior and posterior superior temporal sulcus, and the inferior frontal gyrus. Fearful expressions in human and avatar faces evoked similar responses in the amygdala. We did not find different responses to neutral human and avatar expressions. Our results highlight differences, but also similarities in the processing of fearful human expressions and fearful avatar expressions even if they are designed to be highly anthropomorphic and animated with motion tracking. This has important consequences for research using dynamic avatars, especially when processes are investigated that involve cortical and subcortical regions.

An Extensible Game Engine to Develop Animated Facial Avatars in 3D Virtual Environment

Avatar facial expression and animation in 3D Collaborative Virtual Environment (CVE) systems are reconstructed through a complex manipulation of all details that compose it like muscles, bones and wrinkles in 3D space. The need for a fast and easy reconstruction approach has emerged out in the recent years due to its application in various domains; 3D disaster management and military training etc. These details simulation must be as realistic as possible to convey different emotions according to the constantly changing situations in CVE during the runtime. For example, in 3D disaster management, it is important to use dynamic avatar emotions; firefighters should be frightened when dealing with a fire disaster and smiling when treating injures and evacuating habitants. However, the solution of facial animation remains a challenge that restricts the rapid and ease development of facial animation systems. In this work, the author presents extensible game engine architecture to easily produce real-time facial animations using a script atomic action without having to deal with control structures and 3D programing language. The proposed architecture defines various controllers, object behaviors, tactical and graphics rendering, and collision effects to quickly design 3D virtual environment. Firstly, the author gives the concept of atomic expression, and the method to build a parametrized script file according to the atomic expression. Then the author shows the validity of the generated expressions based on the MPEG-4 facial animation framework. Finally, the feasibility of the proposed architecture is tested via a firefighter scenario. The author's approach has the advantages over previous techniques of fitting directly an easy and faster technology with a high degree of programming independence. The author also minimizes the interaction with the game engine during the runtime by injecting dynamically the XML file into the game engine without stopping or restarting the engine.

A Dynamic Platform for Developing 3D Facial Avatars in a Networked Virtual Environment

Avatar facial expression and animation in 3D collaborative virtual environment (CVE) systems are reconstructed through a complex manipulation of muscles, bones, and wrinkles in 3D space. The need for a fast and easy reconstruction approach has emerged in the recent years due to its application in various domains: 3D disaster management, virtual shopping, and military training. In this work we proposed a new script language based on atomic parametric action to easily produce real-time facial animation. To minimize use of the game engine, we introduced script-based component where the user introduces simple short script fragments to feed the engine with a new animation on the fly. During runtime, when an embedded animation is required, an xml file is created and injected into the game engine without stopping or restarting the engine. The resulting animation method preserves the real-time performance because the modification occurs not through the modification of the 3D code that describes the CVE and its objects but rather through modification of the action scenario that rules when an animation happens or might happen in that specific situation.

The Influence of Beta Signal toward Emotion Classification for Facial Expression Control through EEG Sensors

The important role of communication process between human and computer have been increased in the recent years. In this paper, the main focus on analyzing human brain signals to create a natural interaction between human brain and virtual human. The method of this study base on reading brain signals then classifies the signals in order to represent it as an avatar facial expression. These signals are associated with the inner emotion of the user. In order to get a real interaction between the internal emotion of a user and avatar facial expression, signal speed is used to clarify the difference of user situation for two emotions: happy and sad. The interactive process based on the relationship between human emotion and the velocity of brain signal from the result ΔV for Z1 is faster than ΔV for Z2. In this case, the velocity of sad emotion will be faster than happy emotion. As a result, this study shows a range of speed for each emotion which can be used to specify and represent the internal emotion of a user to create a natural interaction with virtual human. These results can be more realistic because it specifies the average of speed for each emotion.

Consumer reviews: reviewer avatar facial expression and review valence

PurposeThe purpose of this paper is to explore how the facial expression of the reviewer's avatar interacts with the valence of the consumer review to influence consumer purchase decisions.Design/methodology/approachA 2 (facial expression of the reviewer's avatar)× 2 (valence of the consumer review) between‐subjects online experimental design was used.FindingsIt was found that when the consumer review was positive, participants exposed to the reviewer's angry‐looking avatar were more likely to attribute the review to the product's performance than those exposed to the happy‐looking avatar. The causal attribution toward product performance, in turn, influenced the strength of intention to purchase the brand positively. When the consumer review was negative, however, there were no differential effects between the happy‐looking and the angry‐looking avatars.Originality/valueThis study contributes to the literature on consumer reviews by identifying an important source characteristic that consumers consider when processing consumer reviews – the facial expression of the reviewer's avatar.

EmoHeart: Conveying Emotions in Second Life Based on Affect Sensing from Text

The 3D virtual world of “Second Life” imitates a form of real life by providing a space for rich interactions and social events. Second Life encourages people to establish or strengthen interpersonal relations, to share ideas, to gain new experiences, and to feel genuine emotions accompanying all adventures of virtual reality. Undoubtedly, emotions play a powerful role in communication. However, to trigger visual display of user's affective state in a virtual world, user has to manually assign appropriate facial expression or gesture to own avatar. Affect sensing from text, which enables automatic expression of emotions in the virtual environment, is a method to avoid manual control by the user and to enrich remote communications effortlessly. In this paper, we describe a lexical rule-based approach to recognition of emotions from text and an application of the developed Affect Analysis Model in Second Life. Based on the result of the Affect Analysis Model, the developed EmoHeart (“object” in Second Life) triggers animations of avatar facial expressions and visualizes emotion by heart-shaped textures.

Avatar culture: cross-cultural evaluations of avatar facial expressions

Avatars are increasingly used to express our emotions in our online communications. Such avatars are used based on the assumption that avatar expressions are interpreted universally among all cultures. This paper investigated cross-cultural evaluations of avatar expressions designed by Japanese and Western designers. The goals of the study were: (1) to investigate cultural differences in avatar expression evaluation and apply findings from psychological studies of human facial expression recognition, (2) to identify expressions and design features that cause cultural differences in avatar facial expression interpretation. The results of our study confirmed that (1) there are cultural differences in interpreting avatars’ facial expressions, and the psychological theory that suggests physical proximity affects facial expression recognition accuracy is also applicable to avatar facial expressions, (2) positive expressions have wider cultural variance in interpretation than negative ones, (3) use of gestures and gesture marks may sometimes cause counter-effects in recognizing avatar facial expressions.

Improvement in Multi-User Communication Systems Using an Avatar's Facial Expression Features

Improvement in Multi-User Communication Systems Using an Avatar's Facial Expression Features

Amygdala activation at 3T in response to human and avatar facial expressions of emotions

Facial expressions of emotions are important in nonverbal communication. Although numerous neural structures have been identified to be involved in emotional face processing, the amygdala is thought to be a core moderator. While previous studies have relied on facial images of humans, the present study is concerned with the effect of computer-generated (avatar) emotional faces on amygdala activation. Moreover, elicited activation patterns in response to viewing avatar faces are compared with the neuronal responses to human facial expressions of emotions. Twelve healthy subjects (five females) performed facial emotion recognition tasks with optimized 3T event-related fMRI. Robust amygdala activation was apparent in response to both human and avatar emotional faces, but the response was significantly stronger to human faces in face-sensitive structures, i.e. fusiform gyri. We suggest that avatars could be a useful tool in neuroimaging studies of facial expression processing because they elicit amygdala activation similarly to human faces, yet have the advantage of being highly manipulable and fully controllable. However, the finding of differences between human and avatar faces in face-sensitive regions indicates the presence of mechanisms by which human brains can differentiate between them. This mechanism merits further investigation.

MAUI avatars: Mirroring the user's sensed emotions via expressive multi-ethnic facial avatars

In this paper we describe the multimodal affective user interface (MAUI) we created to capture its users’ emotional physiological signals via wearable computers and visualize the categorized signals in terms of recognized emotion. MAUI aims at (1) giving feedback to the users about their emotional states via various modalities (e.g. mirroring the users’ facial expressions and describing verbally the emotional state via an anthropomorphic avatar) and (2) animating the avatar's facial expressions based on the users’ captured signals. We first describe a version of MAUI which we developed as an in-house research tool for developing and testing affective computing research. We also discuss applications for which building intelligent user interfaces similar to MAUI can be useful and we suggest ways of adapting the MAUI approach to fit those specific applications.

Experimental Results of Affective Valence and Arousal to Avatar's Facial Expressions

The objectives of this study were to propose a method of presenting dynamic facial expressions to experimental subjects, in order to investigate human perception of avatar's facial expressions of different levels of emotional intensity. The investigation concerned how perception varies according to the strength of facial expression, as well as according to an avatar's gender. To accomplish these goals, we generated a male and a female virtual avatar with five levels of intensity of happiness and anger using a morphing technique. We then recruited 16 normal healthy subjects and measured each subject's emotional reaction by scoring affective arousal and valence after showing them the avatar's face. Through this study, we were able to investigate human perceptual characteristics evoked by male and female avatars' graduated facial expressions of happiness and anger. In addition, we were able to identify that a virtual avatar's facial expression could affect human emotion in different ways according to the avatar's gender and the intensity of its facial expressions. However, we could also see that virtual faces have some limitations because they are not real, so subjects recognized the expressions well, but were not influenced to the same extent. Although a virtual avatar has some limitations in conveying its emotion using facial expressions, this study is significant in that it shows that a new potential exists to use or manipulate emotional intensity by controlling a virtual avatar's facial expression linearly using a morphing technique. Therefore, it is predicted that this technique may be used for assessing emotional characteristics of humans, and may be of particular benefit for work with people with emotional disorders through a presentation of dynamic expression of various emotional intensities.

Mimics: a symbolic conflict/cooperation simulation program, with embedded protocol recording and automatic psychometric assessment.

This paper describes an interactive software environment designed as a social interaction simulator with embedded comprehensive recording and flexible assessment facilities. Using schematized visual sketches similar to cross-cultural facial universals (Ekman, 1999), Mimics (Shmelyov & Aidman, 1997) employs a computer-game-like scenario that requires the subject to identify with an avatar and navigate it through a playing field inhabited by hosts who display a range of facial expressions. From these expressions (which are highly consequential), the player has to anticipate the hosts' reactions to the avatar (which may vary from friendly to obstructing or aggressive) and choose between negotiating with a host (by altering the avatar's facial expression), attacking it, or searching for an escape route. Comprehensive recording of player moves and interactions has enabled computation of several finegrained indices of interactive behavior, such as aggressive response styles, efficiency, and motivation in conflict/cooperation contexts. Initial validation data and potential applications of the method in the assessment of personality and social behavior are discussed.