Multimodal Emotion Expressions of Virtual Agents, Mimic and Vocal Emotion Expressions and Their Effects on Emotion Recognition

Liebold, Benny; Ohler, Peter

doi:10.1109/acii.2013.73

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…Vinayagamoorthy et al [64] describe models of individual characters' emotions and personalities, models of interpersonal behaviors, and methods for generating emotions using facial expressions. Liebold et al [33] observe that multimodal (e.g., a combination of verbal cues and facial expression) expressions of emotions yield the highest recognition 2) real-time generation of virtual agents (EVA) with desired emotional characteristics using expressive features. We obtain emotion labels for a set of motioncaptured gaits with a user study and model the gait emotion association metric using these labels.…”

Section: Emotions Of Virtual Agentsmentioning

confidence: 99%

EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze

Randhavane

Bera

Kapsaskis

et al. 2019

ACM Symposium on Applied Perception 2019

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We present a novel, real-time algorithm, EVA, for generating virtual agents with various perceived emotions. Our approach is based on using Expressive Features of gaze and gait to convey emotions corresponding to happy, sad, angry, or neutral. We precompute a data-driven mapping between gaits and their perceived emotions. EVA uses this gait emotion association at runtime to generate appropriate walking styles in terms of gaits and gaze. Using the EVA algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual agents in real-time with known emotional characteristics. We have evaluated the benefits in different multi-agent VR simulation environments. Our studies suggest that the use of expressive features corresponding to gait and gaze can considerably increase the sense of presence in scenarios with multiple virtual agents.

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Section: Emotions Of Virtual Agentsmentioning

confidence: 99%

EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze

Randhavane

Bera

Kapsaskis

et al. 2019

ACM Symposium on Applied Perception 2019

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“…McHugh et al [43] explored how different body postures influenced the emotion perception of individual agents in crowds. Clavel et al [12] studied the combined effect of faces and postures of virtual agents on emotion perception, and Liebold et al [39] generalized this to include combinations of other modalities such as verbal cues and faces. More recently, Randhavane et al [61] developed an empirical mapping between gait and gaze features and different emotions to generate emotionally expressive virtual agents.…”

Section: Emotional Expressiveness In Virtual Agentsmentioning

confidence: 99%

Generating Emotive Gaits for Virtual Agents Using Affect-Based Autoregression

Bhattacharya,

Rewkowski,

Guhan

et al. 2020

Preprint

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“…As the world increasingly uses digital and virtual platforms for everyday communication and interactions, there is a heightened need to create highly realistic virtual agents endowed with social and emotional intelligence. Interactions between humans and virtual agents are being used to augment traditional human-human interactions in different applications, including online learning [37,39,59], virtual interviewing and counseling [6,16], virtual social interactions [24,35,40,56], and large-scale virtual worlds [50]. Humanhuman interactions rely heavily on a combination of verbal communications (the text), inter-personal relationships between the people involved (the context), and more subtle non-verbal face and body expressions during communication (the subtext) [32,41].…”

Section: Introductionmentioning

confidence: 99%

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

Bhattacharya,

Rewkowski,

Banerjee

et al. 2021

Preprint

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We present Text2Gestures, a transformer-based learning method to interactively generate emotive full-body gestures for virtual agents aligned with natural language text inputs. Our method generates emotionally expressive gestures by utilizing the relevant biomechanical features for body expressions, also known as affective features. We also consider the intended task corresponding to the text and the target virtual agents' intended gender and handedness in our generation pipeline. We train and evaluate our network on the MPI Emotional Body Expressions Database and observe that our network produces state-of-the-art performance in generating gestures for virtual agents aligned with the text for narration or conversation. Our network can generate these gestures at interactive rates on a commodity GPU. We conduct a web-based user study and observe that around 91% of participants indicated our generated gestures to be at least plausible on a five-point Likert Scale. The emotions perceived by the participants from the gestures are also strongly positively correlated with the corresponding intended emotions, with a minimum Pearson coefficient of 0.77 in the valence dimension.

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Multimodal Emotion Expressions of Virtual Agents, Mimic and Vocal Emotion Expressions and Their Effects on Emotion Recognition

Cited by 16 publications

References 15 publications

EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze

EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze

Generating Emotive Gaits for Virtual Agents Using Affect-Based Autoregression

Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents

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