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We explore user-agnostic and user-specific mapping techniques for manipulating avatars using hand gestures in virtual reality (VR). User-agnostic mapping allows users to control various avatars based on common user agreements, while user-specific mapping adapts to individual preferences using few-shot adaptation. Both approaches use supervised learning with paired datasets of motion data. Our evaluation, including quantitative assessments and user studies, shows that both techniques offer finer control, require less physical effort, and provide higher user satisfaction compared to existing methods. However, only a few users preferred user-specific mapping, indicating that the benefits of personalized mapping may vary. Finally, we demonstrate our methods’ ability to manipulate avatars with variable joint structures, surpassing current methods.

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Hand2Any: Hand-to-Any Motion Mapping with Few-Shot User Adaptation for Avatar Manipulation

  • 会议论文

  • 发表日期：

    2025年

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  • Riku Shinohara,
  • Atsushi Hashimoto,
  • Tadashi Kozuno,
  • Shigeo Yoshida,
  • Yutaro Hirao,
  • Monica Perusquía-Hernández,
  • Hideaki Uchiyama,
  • Kiyoshi Kiyokawa

摘要

We explore user-agnostic and user-specific mapping techniques for manipulating avatars using hand gestures in virtual reality (VR). User-agnostic mapping allows users to control various avatars based on common user agreements, while user-specific mapping adapts to individual preferences using few-shot adaptation. Both approaches use supervised learning with paired datasets of motion data. Our evaluation, including quantitative assessments and user studies, shows that both techniques offer finer control, require less physical effort, and provide higher user satisfaction compared to existing methods. However, only a few users preferred user-specific mapping, indicating that the benefits of personalized mapping may vary. Finally, we demonstrate our methods’ ability to manipulate avatars with variable joint structures, surpassing current methods.