K. Brandstätter, B. J. Congdon and A. Steed, "Do you read me? (E)motion Legibility of Virtual Reality Character Representations," 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Bellevue, WA, USA, 2024, pp. 299-308, doi: 10.1109/ISMAR62088.2024.00044.
We compared the body movements of five virtual reality (VR) avatar representations in a user study (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.
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