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Virtual sport coaches guide users through their physical activity and provide motivational support. Users’ motivation can rapidly decay if the movements of the virtual coach are too stereotyped. Kinematics patterns generated while performing a predefined fitness movement can elicit and help to prolong users’ interaction and interest in training. Hu...
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... particular interest for this paper are two diagonals: the reliability diagonal (using Spearman Brown formula) and the diagonal which indicate the inter-method correlation: they provide indication about the inter-method convergent validity. Table 2 presents these item reliabilities and inter-method correlations. Only the neuroticism scale showed no convergent validity across methods (the inter-method correlation is only about r=.243) and a very poor reliability in the short form of the test. ...
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... residuals from these regressions form the specific part of the evaluation. Correlations between observed and specific components for each scale are presented in Table 2 along with confidence intervals. For the rest of the analysis, we keep the specific components that correlate significantly with their observed part. ...
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... The Quantity of Motion (QoM) is a value between 0 and 1 that estimates the total amount of motion of the body in the 2D plane of the camera. It is often considered a measure of kinetic energy and recurrently associated with emotional arousal in affective situations 75 . We used the motion history images algorithm (MHI) 64 , which provides an image template that shows the recency of motion in a sequence. ...
Postural interaction is of major importance during job interviews. While several prototypes enable users to rehearse for public speaking tasks and job interviews, few of these prototypes support subtle bodily interactions between the user and a virtual agent playing the role of an interviewer. The design of our system is informed by a multimodal corpus that was previously collected. In this paper, we explain how we were inspired by these video recordings of human interviewers to build a library of motion-captured movements that interviewers are most likely to display. We designed a fully automatic interactive virtual agent able to display these movements in response to the bodily movements of the user. Thirty-two participants presented themselves to this virtual agent during a simulated job interview. We focused on the self-presentation task of the job interview, the virtual agent being listening. Participants stood on a force platform that recorded the displacements of their center of pressure to assess the postural impact of our design. We also collected video recordings of their movements and computed the contraction index and the quantity of motion of their bodies. We explain the different hypotheses that we made concerning (1) the comparison between the performance of participants with human interviewers and the performance of participants with virtual interviewers, (2) the comparison between mirror and random postural behaviors displayed by a female versus a male virtual interviewer, and (3) the correlation between the participants' performance and their personality traits. Our results suggest that users perceive the simulated self-presentation task with the virtual interviewer as threatening and as difficult as the presentation task with the human interviewers. Furthermore, when users interact with a virtual interviewer that mirrors their postures, these users perceive the interviewer as being affiliative. Finally, a correlation analysis showed that personality traits had a significant relation to the postural behaviors and performance of the users during their presentation.
... [Oosterhof and Todorov 2008]), linguistic style (e.g. [Walker et al. 1997]), finger movements [Wang et al. 2016], kinematic pattern [Giraud et al. 2015], walk cycle [Thoresen et al. 2012], and correlations with parameters of Laban Movement Analysis [Durupinar et al. 2016]. ...
... Oosterhof and Todorov [2008] found that only two judgments suffice to explain personality perceptions based on facial structure. Giraud et al. [2015] found that two judgments are sufficient to explain personality perceptions based on coaches performing kinematic patterns. Our work suggests that variation in gesture movement may also lead to personality judgments in only two dimensions. ...
Applications such as virtual tutors, games, and natural interfaces increasingly require animated characters to take on social roles while interacting with humans. The effectiveness of these applications depends on our ability to control the social presence of characters, including their personality. Understanding how movement impacts the perception of personality allows us to generate characters more capable of fulfilling this social role. The two studies described herein focus on gesture as a key component of social communication and examine how a set of gesture edits, similar to the types of changes that occur during motion warping, impact the perceived personality of the character. Surprisingly, when based on thin-slice gesture data, people's judgments of character personality mainly fall in a 2D subspace rather than independently impacting the full set of traits in the standard Big Five model of personality. These two dimensions are plasticity, which includes extraversion and openness, and stability, which includes emotional stability, agreeableness, and conscientiousness. A set of motion properties is experimentally determined that impacts each of these two traits. We show that when these properties are systematically edited in new gesture sequences, we can independently influence the character's perceived stability and plasticity (and the corresponding Big Five traits), to generate distinctive personalities. We identify motion adjustments salient to each judgment and, in a series of perceptual studies, repeatedly generate four distinctly perceived personalities. The effects extend to novel gesture sequences and character meshes, and even largely persist in the presence of accompanying speech. This paper furthers our understanding of how gesture can be used to control the perception of personality and suggests both the potential and possible limits of motion editing approaches.
We express our personality through verbal and nonverbal behavior. While verbal cues are mostly related to the semantics of what we say, nonverbal cues include our posture, gestures, and facial expressions. Appropriate expression of these behavioral elements improves conversational virtual agents’ communication capabilities and realism. Although previous studies focus on co-speech gesture generation, they do not consider the personality aspect of the synthesized animations. We show that automatically generated co-speech gestures naturally express personality traits, and heuristics-based adjustments for such animations can further improve personality expression. To this end, we present a framework for enhancing co-speech gestures with the different personalities of the Five-Factor model. Our experiments suggest that users perceive increased realism and improved personality expression when combining heuristics-based motion adjustments with co-speech gestures.
Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co‐speech gestures is a long‐standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non‐periodic nature of human co‐speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep‐learning‐based generative models that benefit from the growing availability of data. This review article summarizes co‐speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule‐based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non‐linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human‐like motion; grounding the gesture in the co‐occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
Many areas in computer science are facing the need to analyze, quantify and reproduce movements expressing emotions. This paper presents a systematic review of the intelligible factors involved in the expression of emotions in human movement and posture. We have gathered the works that have studied and tried to identify these factors by sweeping many disciplinary fields such as psychology, biomechanics, choreography, robotics and computer vision. These researches have each used their own definitions, units and emotions, which prevents a global and coherent vision. We propose a meta-analysis approach that cross-references and aggregates these researches in order to have a unified list of expressive factors quantified for each emotion. A calculation method is then proposed for each of the expressive factors and we extract them from an emotionally annotated animation dataset: Emilya. The comparison between the results of the meta-analysis and the Emilya analysis reveals high correlation rates, which validates the relevance of the quantified values obtained by both methodologies. The analysis of the results raises interesting perspectives for future research in affective computing.
When designing objects, designers attempt to communicate the purpose and meaning of that object to users using various factors such as visual appearance (aesthetic), practical interaction elements (product semantics) and meanings beyond the practical product interaction (semiotics). This study sought to confirm the previous deductively-developed soma-semiotic framework, whose purpose was to understand and ultimately predict the emotional impact of different design elements on users, using one specifically designed object, Fruit Bowl (FB). The purpose of the study reported in this paper was to compare the theoretically derived emotional responses to FB from the soma-semiotic framework with empirically derived data from users in order to improve the framework. Sixty participants evaluated the meaning and emotion conveyed by FB as well as self-reported their own experienced emotions under two scenarios. The framework predicted that FB would convey joy in a first scenario, and amusement in a second scenario based on different movements. Using a weighted vector analysis based on Russell's two-dimensional Circumplex of emotions, users identified that the overall emotion of the first scenario to be similar to the predicted emotion. This was attributed mostly to the bouncy movement of the bowl and its visual aesthetic. However, in the second scenario the overall rating was calm/impressed; rather than humour. The abstract design did not favour users making the same associations as the designer. We recommend that the soma-semiotic framework be revised to include aesthetic, in addition to semiotic and semantic, elements as determinants of user interpretations and reactions to designed objects.
