Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models (2025)

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.

## introduction emotion recognition has been widely studied in both natural language processing and computer vision, with applications ranging from sentiment analysis to human-robot interaction. while facial expressions are often considered the primary channel for conveying emotion, they are not always reliable-for example, when faces are distant, obscured, or deliberately masked. in such cases, the body (torso and limbs) frequently communicates emotion more clearly than the face. for instance, observers often struggle to distinguish between positive and negative feelings from isolated facial expressions, but can do so accurately from bodily cues @xcite . these findings highlight that bodily reactions are central to how emotions are expressed and perceived @xcite ); yet, this important dimension remains underexplored in much of the current emotion recognition research. the concept of embodied emotion provides a richer framework for understanding the affective states and making holistic use of all bodily parts for emotion recognition. rooted in psychology and cognitive science @xcite , embodiment theories argue that emotions are not purely mental phenomena but are deeply intertwined with bodily experience and physical response. in parallel, computational work has shown that embodied signals are also crucial for interpreting emotions in text, where bodily actions and sensations often convey affect beyond lexical sentiment @xcite . together, these insights suggest that posture, gesture, and physiological change are integral to emotional meaning @xcite . empirical studies further show systematic correlation between specific posture and mood-for example, an upright stance can indicate elevated mood, whereas a slumped shoulder correlates with depression and low arousal @xcite . in this work, we propose to bridge the gap of embodied emotions research with vision modality by utilizing prevalent large vision-language models (lvlms) and their capabilities. while prior approaches to the task of emotion recognition have often relied on models trained and fine-tuned on a variety of features @xcite , our work utilizes lvlms in a zero-shot setting to output multi-layered textual descriptions for embodied emotions, as shown in figure 1 . these models do not receive any taskspecific training, but are guided by carefully crafted instructions that elicit the underlying emotional manifestations in images. as far as we are concerned, this is the first work in the area of multimodal analysis for embodied emotions. we release the code and outputs of elena publicly. 1 we highlight our contributions as below: ## related work emotion understanding is a long-standing task in nlp, rooted in the field of affective computing research @xcite @xcite . psychology and neuroscience theories have long argued that bodily states (e.g., posture and physiological re-actions) are integral to emotions @xcite @xcite . prior nlp work has begun to consider the physical expressions of emotion in text. @xcite analyzed how authors depict characters' body movements and sensations (e.g., frowning and heart pounding) to imply emotion, and @xcite identified emotion-related components based on a cognitive model. the rise of large language models (llms) has prompted a re-examination of emotion understanding in nlp @xcite . however, purely text-based models may overlook nonverbal signals that are critical to emotional expressions @xcite . vision-language models (vlms) extend emotion understanding with visual context and commonsense reasoning @xcite , yet evaluations on evoked emotions reveal biases towards specific categories and prompt sensitivity @xcite . visual affective computing remains dominated by facial cues @xcite . in parallel, body-centric approaches model affect via posture and gesture features @xcite and skeleton-based relations among body parts @xcite @xcite , but progress is limited by scarce, richly annotated datasets. departing from these facial and skeleton-centric pipelines, we address embodied emotion in images by instructing lvlms to generate structured, body-part-grounded narratives alongside an ekman emotion label, and we explicitly disentangle facial versus non-facial evidence via face masking and attention-map diagnostics. the recently introduced cheer dataset @xcite represents a first step in this direction-it contains 7,300 instances of body part mentions in narratives, labeled for whether they signal an underlying emotion, e.g., "her hands trembled with fear." this work was carried forward by @xcite , who converted the previous binary task to ekman's six emotion categories, along with improved metrics compared to supervised methods using best-worst scaling. however, the task of embodied recognition remains understudied in vision-language models, as well as the evaluation of the influence of facial parts of the body on emotional expression. inferring affect purely from bodily posture, movements, and physiological state in images remains an open challenge, which motivates the focus of our study. ## methodology we propose a zero-shot structured prompting approach that utilizes large vision-language models to systematically interpret emotions through bodily expressions. our methodology includes detailed prompt design, face-masking experiments, attention analysis, and comparative evaluations to robustly assess the models' capability in recognizing embodied emotions. ## results and analysis with our methodology in place for eliciting embodied emotional indicators from lvlms, we move to the experimental analysis and results. we evaluate elena's performance through two key comparisons: first, we assess how our framework performs against baseline naive prompts across different models and datasets, and second, we examine how face masking affects the model behavior and attention patterns in embodied emotion recognition tasks. we conclude with attention visualization analysis, breakdowns of emotion-specific performance and comparison to a modular baseline. ## conclusion in this work, we introduced elena, a framework that employs lvlms in a zero-shot setting to generate structured narratives that anatomize embodied emotions. our experiments reveal that elena yields credible improvements over naive prompts, demonstrating the models' ability to adapt in both unmasked and masked settings despite information loss. our experiments showed that the gemini 2.5 flash model generally outperformed other models across most datasets. however, labels like disgust proved particularly challenging to identify from bodily expressions. the attention analysis provides a diagnosis by documenting persistent facial bias. it establishes an essential empirical foundation that enables the community to develop targeted solutions, such as fine-tuning with redirected attention mechanisms, for accurate anatomical understanding of embodied emotions.