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arXiv · 2509.19595

Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models

Mohammad Saim·Phan Anh Duong·Cat Luong·Aniket Bhanderi·Tianyu Jiang
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Abstract

The embodiment of emotional reactions from body parts contains rich information about our affective experiences. We propose a framework that utilizes state-of-the-art large vision-language models (LVLMs) to generate Embodied LVLM Emotion Narratives (ELENA). These are well-defined, multi-layered text outputs, primarily comprising descriptions that focus on the salient body parts involved in emotional reactions. We also employ attention maps and observe that contemporary models exhibit a persistent bias towards the facial region. Despite this limitation, we observe that our employed framework can effectively recognize embodied emotions in face-masked images, outperforming baselines without any fine-tuning. ELENA opens a new trajectory for embodied emotion analysis across the modality of vision and enriches modeling in an affect-aware setting.

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Mohammad Saim, Phan Anh Duong, Cat Luong, Aniket Bhanderi, Tianyu Jiang. 2025-09-23. Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models. https://arxiv.org/abs/2509.19595

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