Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions

2026-06-27

AI Summary

This study addresses the challenge of generating culturally specific emotional body motions without explicit emotion–motion alignment supervision. Leveraging motion capture data from Japanese actors, the authors propose a Transformer-based generative model that synthesizes affective gestures directly from discrete emotion labels. This work presents the first approach to culture-sensitive emotional motion generation in the absence of explicit alignment cues and further extends the framework to support downstream tasks such as emotion recognition enhancement, prototypical motion extraction, and smooth transitions across emotion intensities. Experimental results demonstrate that the generated motions achieve a machine recognition accuracy of 22.80% and a human evaluation score of 24.91%, confirming the method’s effectiveness and practical potential in affective computing and virtual character animation.

Abstract

Emotional body motion expressions are an essential element of non-verbal communication. Effectively conveying these expressions through technology is of utmost importance, for example, with virtual reality avatars and in social robotics. Recent advances in generative models have opened new opportunities for advancing research on emotional body motion learning. However, generating accurate emotional expression representations is challenging, given the subtlety of emotional cues, individual variability, and cultural differences. We investigate whether a generative model can implicitly learn emotional body motions directly from culturally grounded motion-capture data, without explicit emotion-motion guidance. Using a dataset of emotional performances by 49 Japanese actors, we trained a Transformer-based generative model to generate expressive motions conditioned on 13 discrete emotion labels. We evaluate the generated motions from two perspectives: (1) an LSTM-based classifier to assess recognizability by machine observers, achieving a recognition accuracy of 22.80%, and (2) a human perception study with Japanese raters to assess alignment with human affective interpretations, yielding a recognition accuracy of 24.91%. Beyond these, we evaluate the utility of generative modeling for three practical tasks: augmenting emotion recognition models, extracting representative emotion-specific motion patterns, and synthesizing smooth transitions between emotion intensities. Our findings highlight the potential of implicit, data-driven generative modeling to enhance affective computing applications and our understanding of emotion expressions.

Problem

• emotional body motion
• generative learning
• affective computing
• motion generation
• non-verbal communication

Innovation

• generative modeling
• emotional body motion
• Transformer-based generation
• implicit learning
• affective computing