GenFaceUI: Meta-Design of Generative Personalized Facial Expression Interfaces for Intelligent Agents

TL;DR

This paper introduces GenFaceUI, a framework and tool for designing generative facial expression interfaces for intelligent agents, emphasizing control, coherence, and alignment through a meta-design approach.

Contribution

It presents a novel meta-design framework and a proof-of-concept tool for creating customizable facial expression interfaces, addressing control and consistency challenges.

Findings

Designers perceived improved controllability and consistency.

Qualitative study revealed needs for visual mechanisms and explanations.

Framework and tool provide new avenues for facial expression interface design.

Abstract

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.