Prompt-to-Animation: Generating Cognitively-Grounded Facial Expressions with LLMs

Expressive facial animation depends on models that can convey subtle, context'dependent emotions. Procedural methods often rely on manually-tuned heuristics, while data'driven techniques are constrained by the diversity of their training data. This paper explores the zero-shot potential of large language models (LLMs) to generate facial animations. Using the cognitively-grounded Ortony-Clore-Collins (OCC) model as a framework, we designed 110 text-based scenarios and evaluated the ability of different LLMs - Gemini-2.5 Pro, GPT-4o, and Llama 3.1-8b - to generate corresponding facial animations using the Facial Action Coding System (FACS). A perceptual user study confirmed that animations from Gemini-2.5 Pro were highly recognizable, with participants successfully matching facial expressions to the correct context at rates significantly above chance. A quantitative analysis of the generated Action Units (AUs) indicated both consistency within each OCC emotion category and diversity across different scenarios. Further analysis revealed that the emotional expressions group into six clusters: happiness, sadness, anger/disgust, fear/surprise, shame, and neutral. This work demonstrates a viable, lightweight pipeline connecting textual narrative directly to motion generation without requiring custom training or large-scale motion capture datasets.