This work investigates generative facial expression interfaces for intelligent agents from a meta-design perspective. We propose the Generative Personalized Facial Expression Interface (GPFEI) framework, which organizes rule-bounded spaces, character identity, and context--expression mapping to address challenges of control, coherence, and alignment in run-time facial expression generation. To operationalize this framework, we developed GenFaceUI, a proof-of-concept tool that enables designers to create templates, apply semantic tags, define rules, and iteratively test outcomes. We evaluated the tool through a qualitative study with twelve designers. The results show perceived gains in controllability and consistency, while revealing needs for structured visual mechanisms and lightweight explanations. These findings provide a conceptual framework, a proof-of-concept tool, and empirical insights that highlight both opportunities and challenges for advancing generative facial expression interfaces within a broader meta-design paradigm.
@article{arxiv.2602.11055,
title = {GenFaceUI: Meta-Design of Generative Personalized Facial Expression Interfaces for Intelligent Agents},
author = {Yate Ge and Lin Tian and Yi Dai and Shuhan Pan and Yiwen Zhang and Qi Wang and Weiwei Guo and Xiaohua Sun},
journal= {arXiv preprint arXiv:2602.11055},
year = {2026}
}