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

• URL: http://arxiv.org/abs/2509.19595v1
• Date: Tue, 23 Sep 2025 21:34:57 GMT
• Title: Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models
• Authors: Mohammad Saim, Phan Anh Duong, Cat Luong, Aniket Bhanderi, Tianyu Jiang,
• Abstract summary: We propose a framework to generate Embodied LVLM Emotion Narratives (ELENA)<n>These are well-defined, multi-layered text outputs that focus on the salient body parts involved in emotional reactions.<n>We observe that our employed framework can effectively recognize embodied emotions in face-masked images, outperforming baselines without any fine-tuning.
• Score: 1.8349570933241344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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