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Demo: gesture based interaction with the Hololens 2

Published: 20 September 2023 Publication History

Abstract

Gesture recognition is one of the default interaction modalities in many XR applications, although the gesture types recognized by many applications is typically limited to few static poses. In this demo we show that a recent network-based solution for online, sliding window, gesture classification from hand pose streams (On-Off deep Multi-View Multi-Task) can be used for the simultaneous detection and recognition of heterogeneous gestures, including dynamic coarse and fine grained ones, enabling interaction designers to create novel ways to perform interactive tasks that can be applied to different domains.

References

[1]
Federico Cunico, Marco Emporio, Federico Girella, Andrea Giachetti, Andrea Avogaro, and Marco Cristani. 2023. OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2744–2753.
[2]
Marco Emporio, Ariel Caputo, Andrea Giachetti, Marco Cristani, Guido Borghi, Andrea D’Eusanio, Minh-Quan Le, Hai-Dang Nguyen, Minh-Triet Tran, Felix Ambellan, 2022. SHREC 2022 track on online detection of heterogeneous gestures. Computers & Graphics 107 (2022), 241–251.

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cover image ACM Other conferences
CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter
September 2023
416 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2023

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Author Tags

  1. Gestures
  2. Mid-air Interaction
  3. Neural Networks

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  • Demonstration
  • Research
  • Refereed limited

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CHItaly 2023

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Overall Acceptance Rate 109 of 242 submissions, 45%

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