Brandstatter, K;
Congdon, BJ;
Steed, A;
(2024)
Do you read me? (E)motion Legibility of Virtual Reality Character Representations.
In:
2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
(pp. pp. 299-308).
IEEE: Bellevue, WA, USA.
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Abstract
We compared the body movements of five virtual reality (VR) avatar representations in a user study $(\mathrm{N}=53)$ to ascertain how well these representations could convey body motions associated with different emotions: one head-and-hands representation using only tracking data, one upper-body representation using inverse kinematics (IK), and three full-body representations using IK, motioncapture, and the state-of-the-art deep-learning model AGRoL. Participants' emotion detection accuracies were similar for the IK and AGRoL representations, highest for the full-body motion-capture representation and lowest for the head-and-hands representation. Our findings suggest that from the perspective of emotion expressivity, connected upper-body parts that provide visual continuity improve clarity, and that current techniques for algorithmically animating the lower-body are ineffective. In particular, the deep-learning technique studied did not produce more expressive results, suggesting the need for training data specifically made for social VR applications.
| Type: | Proceedings paper |
|---|---|
| Title: | Do you read me? (E)motion Legibility of Virtual Reality Character Representations |
| Event: | 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) |
| Dates: | 21 Oct 2024 - 25 Oct 2024 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISMAR62088.2024.00044 |
| Publisher version: | https://doi.org/10.1109/ismar62088.2024.00044 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Legged locomotion, Solid modeling, Visualization, Emotion recognition, Accuracy, Tracking, Avatars, Training data, Motion capture, Data models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203417 |
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