User Attention and Behaviour in Virtual Reality Art Encounter
This work addresses the problem of optimizing creative processes in VR for content creators, though it appears incremental as it builds on existing tracking and analysis methods.
The study tackled understanding user attention and behaviors in virtual reality art experiences by developing an abstract VR painting system with eye gaze and movement tracking, revealing a range of activity patterns from 35 participants and using deep learning to analyze connections with audience background.
With the proliferation of consumer virtual reality (VR) headsets and creative tools, content creators have started to experiment with new forms of interactive audience experience using immersive media. Understanding user attention and behaviours in virtual environment can greatly inform creative processes in VR. We developed an abstract VR painting and an experimentation system to study audience encounters through eye gaze and movement tracking. The data from a user experiment with 35 participants reveal a range of user activity patterns in art exploration. Deep learning models are used to study the connections between behavioural data and audience background. New integrated methods to visualise user attention as part of the artwork are also developed as a feedback loop to the content creator.