Ali, Ghazanfar Kim, HwangYoun Hwang, Jae-In 2025-08-20T07:10:50Z 2025-08-20T07:10:50Z 2025-08-20 2025-07 1546-4261 https://pubs.kist.re.kr/handle/201004/153004 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: . English John Wiley & Sons Inc. RIDGE: Rule-Infused Deep Learning for Realistic Co-Speech Gesture Generation Article 10.1002/cav.70034 1 Computer Animation & Virtual Worlds, v.36, no.4 Computer Animation & Virtual Worlds 36 4 N scie scopus 001547879700001 2-s2.0-105012884391 Computer Science, Software Engineering Computer Science Article HCI virtual worlds computer animation co-speech gestures