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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fogarty, John Shotton, Jamie Wood, Erroll Fitzgibbon, Andrew Taylor, Jonathan |
| Abstract | This paper presents ShadowHands - a novel technique for visualizing a remote user's hand gestures using a single depth sensor and hand tracking system. Previous work has shown that making distributed users better aware of each other's gestures facilitates remote collaboration. These systems presented virtual embodiments as a stream of raw 2D or 3D data -- this data is noisy, and requires high bandwidth and favorable camera positions. Instead, our work uses a hand tracker to capture gestures which we visualize with a high-fidelity hand model. Our system is practical, requiring only a single depth sensor placed below the screen, and can be used without per-user calibration. As we use a 3D model rather than raw data, we can augment the hand's appearance to improve saliency and aesthetics. We alpha-blend this visualization over a shared workspace, so the local user perceives the remote user's hand as if they were separated by a transparent display. We conducted an experiment to compare traditional hand embodiments with our new technique, showing a quantitative improvement in selection accuracy and qualitative improvements in feelings of mutual understanding and engagement. |
| Starting Page | 77 |
| Ending Page | 84 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450342483 |
| DOI | 10.1145/2992154.2992169 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-11-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Remote collaboration Hand gestures Hand tracking |
| Content Type | Text |
| Resource Type | Article |
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