AgeStyle: Dynamic age-guided motion transfer in virtual reality

Abstract

Age significantly influences human motor patterns, yet existing virtual reality (VR) systems lack dynamic modelling of these variations. This paper introduces AgeStyle, a versatile framework that integrates age-guided style selection with motion style transfer to convert user-uploaded videos into interactive 3D motion models. Utilizing 2D joint detection and 3D pose estimation, AgeStyle constructs motion representations enhanced by a CLIP-driven Cross-Attention module, capturing the distinct traits of different age groups—child flexibility, adult efficiency, and elderly stability. Our system enables real-time switching between motion styles and perspectives through voice commands, offering an immersive exploration of age-related movements. Quantitative experiments on the XIA dataset demonstrate AgeStyle’s competitive performance in both content preservation and style consistency, achieving average CC and SC++ scores of 7.4 and 14.8, respectively. AgeStyle represents a meaningful advancement in VR character design, with broad potential applications in education, healthcare, rehabilitation, and interactive entertainment. For the demo, please refer to https://youtu.be/eo7Shy0Ukps. The source code of AgeStyle is available at https://github.com/codeozzz/ageStyle.