Presti Paolo, Ruzzon Davide, Galasso Gaia Maria, Avanzini Pietro, Caruana Fausto, Vecchiato Giovanni
Institute of Neuroscience, National Research Council of Italy, Parma, Italy.
Department of Medicine and Surgery, University of Parma Parma, Italy.
Front Neurosci. 2022 Jun 15;16:842433. doi: 10.3389/fnins.2022.842433. eCollection 2022.
Dynamic virtual representations of the human being can communicate a broad range of affective states through body movements, thus effectively studying emotion perception. However, the possibility of modeling static body postures preserving affective information is still fundamental in a broad spectrum of experimental settings exploring time-locked cognitive processes. We propose a novel automatic method for creating virtual affective body postures starting from kinematics data. Exploiting body features related to postural cues and movement velocity, we transferred the affective components from dynamic walking to static body postures of male and female virtual avatars. Results of two online experiments showed that participants coherently judged different valence and arousal levels in the avatar's body posture, highlighting the reliability of the proposed methodology. In addition, esthetic and postural cues made women more emotionally expressive than men. Overall, we provided a valid methodology to create affective body postures of virtual avatars, which can be used within different virtual scenarios to understand better the way we perceive the affective state of others.
人类的动态虚拟表征可以通过身体动作传达广泛的情感状态，从而有效地研究情绪感知。然而，在探索时间锁定认知过程的广泛实验环境中，对保留情感信息的静态身体姿势进行建模的可能性仍然至关重要。我们提出了一种从运动学数据创建虚拟情感身体姿势的新颖自动方法。利用与姿势线索和运动速度相关的身体特征，我们将情感成分从动态行走转移到男性和女性虚拟化身的静态身体姿势上。两项在线实验的结果表明，参与者能够连贯地判断化身身体姿势中的不同效价和唤醒水平，突出了所提出方法的可靠性。此外，审美和姿势线索使女性比男性更具情感表现力。总体而言，我们提供了一种有效的方法来创建虚拟化身的情感身体姿势，可在不同虚拟场景中使用，以更好地理解我们感知他人情感状态的方式。
