title: Vision-Based Hand Gesture Customization from a Single Demonstration
summary: Hand gesture recognition is becoming a more prevalent mode of human-computer
  interaction, especially as cameras proliferate across everyday devices. Despite
  continued progress in this field, gesture customization is often underexplored.
  Customization is crucial since it enables users to define and demonstrate
  gestures that are more natural, memorable, and accessible. However,
  customization requires efficient usage of user-provided data. We introduce a
  method that enables users to easily design bespoke gestures with a monocular
  camera from one demonstration. We employ transformers and meta-learning
  techniques to address few-shot learning challenges. Unlike prior work, our
  method supports any combination of one-handed, two-handed, static, and dynamic
  gestures, including different viewpoints. We evaluated our customization method
  through a user study with 20 gestures collected from 21 participants, achieving
  up to 97% average recognition accuracy from one demonstration. Our work
  provides a viable path for vision-based gesture customization, laying the
  foundation for future advancements in this domain.
paper_authors._id: 65cc424ad6c974694f25a16a
paper_authors.name: Soroush Shahi
paper_authors._id: 65cc424ad6c974694f25a16b
paper_authors.name: Cori Tymoszek Park
paper_authors._id: 65cc424ad6c974694f25a16c
paper_authors.name: Richard Kang
paper_authors._id: 65cc424ad6c974694f25a16d
paper_authors.name: Asaf Liberman
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-14T12:36:17.172Z
paper_authors._id: 64468634f27198480f19e51a
paper_authors.avatarUrl: /avatars/05e8de12dd32bf0798116a42da5a4cda.svg
paper_authors.fullname: Asaf Liberman
paper_authors.type: user
paper_authors.user: libman
paper_authors._id: 65cc424ad6c974694f25a16e
paper_authors.name: Oron Levy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-14T12:36:22.706Z
paper_authors._id: 6512acd94151fb1fa724123c
paper_authors.avatarUrl: /avatars/b994f7184cf921e120b6855382f22903.svg
paper_authors.fullname: Oron Levy
paper_authors.type: user
paper_authors.user: oronlevy
paper_authors._id: 65cc424ad6c974694f25a16f
paper_authors.name: Jun Gong
paper_authors._id: 65cc424ad6c974694f25a170
paper_authors.name: Abdelkareem Bedri
paper_authors._id: 65cc424ad6c974694f25a171
paper_authors.name: Gierad Laput
paper_title: Vision-Based Hand Gesture Customization from a Single Demonstration
paper_summary: Hand gesture recognition is becoming a more prevalent mode of human-computer
  interaction, especially as cameras proliferate across everyday devices. Despite
  continued progress in this field, gesture customization is often underexplored.
  Customization is crucial since it enables users to define and demonstrate
  gestures that are more natural, memorable, and accessible. However,
  customization requires efficient usage of user-provided data. We introduce a
  method that enables users to easily design bespoke gestures with a monocular
  camera from one demonstration. We employ transformers and meta-learning
  techniques to address few-shot learning challenges. Unlike prior work, our
  method supports any combination of one-handed, two-handed, static, and dynamic
  gestures, including different viewpoints. We evaluated our customization method
  through a user study with 20 gestures collected from 21 participants, achieving
  up to 97% average recognition accuracy from one demonstration. Our work
  provides a viable path for vision-based gesture customization, laying the
  foundation for future advancements in this domain.
paper_ai_keywords: transformers meta-learning few-shot learning monocular camera bespoke gestures one-handed gestures two-handed gestures static gestures dynamic gestures different viewpoints user study recognition accuracy