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.
Cite
@article{arxiv.2509.19595,
title = {Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models},
author = {Mohammad Saim and Phan Anh Duong and Cat Luong and Aniket Bhanderi and Tianyu Jiang},
journal= {arXiv preprint arXiv:2509.19595},
year = {2025}
}