RIDGE: Rule‐Infused Deep Learning for Realistic Co‐Speech Gesture Generation
Ghazanfar Ali, H. Kim, Jae‐In Hwang · Computer Animation and Virtual Worlds · 2025
ABSTRACT Co‐speech gestures are essential for natural human communication, yet existing synthesis methods fall short in delivering semantically aligned and contextually appropriate motions. In this paper, we present RIDGE, a hybrid system that combines rule‐based and deep learning approaches to generate realistic gestures for virtual avatars and human‐computer interaction. RIDGE employs a high‐fidelity rule base, generated from motion capture data with the assistance of large language models, to select reliable gesture mappings. When a high‐confidence match is not available, a contrastively trained deep learning model steps in to produce semantically appropriate gestures. Evaluated using a novel Gesture Cluster Affinity (GCA) metric, our system outperforms existing baselines, achieving a GCA score of 0.73 compared to a rule‐based baseline of 0.6 and an end‐to‐end: 0.52, while the ground truth score was 0.90. Detailed analyses of system architecture, data preprocessing, and evaluation methodologies demonstrate RIDGE's potential to enhance gesture synthesis. Project Url: https://www.mrlab.co.kr/research/ridge .