Nesrine Fourati

Upper Body Animation Synthesis for a Laughing Character

Laughter is an important social signal in human communication. This paper proposes a statistical framework for generating laughter upper body animations. These animations are driven by two types of input signals, namely the acoustic segmentation of laughter as pseudo-phoneme sequence and acoustic features. During the training step, our statistical framework learns the relationship between the laughter human motion and the input signals. During the synthesis step, our trained framework synthesizes automatically natural head and torso animations from the input signals. Objective and subjective evaluations were conducted to validate this framework. The results show that our proposed framework is capable of generating laughing upper body movements.

Collection and characterization of emotional body behaviors

This paper addressees two issues in modeling bodily expression of emotions; emotional behaviors collection and expressive movement characterization. In this paper, we describe our body movement coding schema intended to the characterization of bodily emotional expression in different movement tasks. We describe as well the database that we use for the characterization of emotion expression in different movement tasks through the proposed body movement coding schema.

Multi-level classification of emotional body expression

Bodily expression of emotion is recently receiving a growing interest, in particular regarding the study of bodily expression of emotions in daily actions such as walking or knocking at the door. However, previous studies tend to focus on a limited range of actions or emotions. Based on a new motion capture database of emotional body expression in daily actions, we propose a deeper analysis of the expression of emotions in body movement based on different emotions, actions and a wide range of low-level body cues. Random Forest approach is applied to investigate the classification of emotions in different movement tasks and to study the contribution of different types of body cues to the classification of each expressed emotion.

Relevant body cues for the classification of emotional body expression in daily actions

In the context of emotional body expression, previous works mainly focused on perceptual studies to identify the most important expressive cues. Only few studies gave insights on which body cues could be relevant for the classification and the characterization of emotions expressed in body movement. In this paper, we present our Random Forest based feature selection approach for the identification of relevant expressive body cues in the context of emotional body expression classification. We also discuss the ranking of relevant body cues according to each expressed emotion across a set of daily actions.

Facial Expressions of Appraisals Displayed by a Virtual Storyteller for Children

In this paper, we present a framework for an expressive virtual storyteller for children. Our virtual storyteller displays facial expressions of appraisals related to story events. The facial expressions are animated jointly with deictic gestures towards graphical elements representing story events. We describe a preliminary study that we conducted with 23 children. We discuss the impact of facial expressions of appraisals on children’s memorization of story events, their perception of characters’ appraisals, their subjective perception of the virtual storyteller and more generally how emotion combines with joint attention.

Head, Shoulders and Hips Behaviors during Turning

Turning behavior is part of the basic library of motor synergies. It involves a complex interplay between the different body parts. In this study, we investigate the behavior of shoulders, hips and head during walking and turning tasks with various emotional states and different angles. We found that shoulders and hips follow a strong linear relationship during turning with different angles and styles of walk, while the head behavior is affected by these variables.

Perception of Emotions and Body Movement in the Emilya Database

In this paper, we examine the perception of emotions as well as the characterization and the classification of emotional body expressions based on perceptual body cues ratings. Emilya (EMotional body expression In daILY Actions), a database of body expressions of eight emotions (including Neutral) in seven daily actions performed by 11 actors, is used for these purposes. A perceptual study is conducted to explore four issues: 1) how expressed emotions are perceived by humans, 2) how emotion recognition by humans differs across daily actions, 3) how expressed emotions are characterized by humans through body cues, and 4) how emotions are automatically classified based on human rating of body cues. Across all the actions, most of the expressed emotions were correctly identified, but some were confused (e.g., Shame and Sadness). Confusions occurring at the level of emotion perception may be due to a lack of contextual factors (Emilya contains body movement of daily actions without reference to a context), to a similarity of bodily expressions, but also to the lack of other modalities that may contribute to a better recognition of bodily expression of these emotions (e.g., facial expressions). In the paper, we detail and discuss the results from these different studies.