Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2992154.2992169 | ISS |
Keywords | DocType | Citations |
Remote Collaboration, Hand Gestures, Hand Tracking | Conference | 2 |
PageRank | References | Authors |
0.37 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erroll Wood | 1 | 2 | 0.37 |
| Jonathan Taylor | 2 | 103 | 4.93 |
| John Fogarty | 3 | 2 | 0.37 |
| andrew w fitzgibbon | 4 | 8128 | 470.66 |
| Jamie Shotton | 5 | 7571 | 324.72 |