Facial Expression and Behavior Modeling from Digital Avatars to Expressive Humanoid Robots: A Review
Xu Zhang and Longbing Cao. ACM Computing Surveys, 2026.

Endowing humanoid robots with expressive, controllable, and robust facial expressions and behaviors is essential for advancing human-like humanoid-human interaction across social, healthcare, and service domains. However, transforming the semantic richness and flexibility of virtual avatars into the mechanical, real-time, and calibration constraints of physical humanoids remains a fundamental challenge. This survey reviews facial expression and behavior modeling for both and analyzes their virtual-to-physical gaps from a deployment-oriented
perspective. Our review is organized along three axes: (i) modeling paradigms, with emphasis on actuator compatibility and transferability; (ii) expression control strategies, assessed for their potential to enable robust robotic deployment; and (iii) deployment dimensions, which examine the challenges of parameter-to-actuator mapping, calibration, and real-time operation. This survey provides a critical synthesis of the trade-offs between expressive fidelity and hardware feasibility. We analyze persistent gaps between algorithmic advances and embodied realization, and review the datasets, metrics, and evaluation frameworks necessary for advancing expressive humanoids and their facial expression and behavior modeling. Finally, we identify open challenges and future research directions, including multimodal coordination, adaptive personalization, and standardized evaluation, providing guidance for next-generation expressive human-like humanoids, and systematically integrating advances in humanoid modeling with actionable deployment of humanoid facial
expressions and behaviors.