Personified and Multistate Camera Motions for First-Person Navigation in Desktop Virtual Reality

In this paper we introduce novel 'Camera Motions' (CMs) to improve the sensations related to locomotion in virtual environments (VE). Traditional Camera Motions are artificial oscillating motions applied to the subjective viewpoint when walking in the VE, and they are meant to evoke and reproduce the visual flow generated during a human walk. Our novel camera motions are: (1) multistate, (2) personified, and (3) they can take into account the topography of the virtual terrain. Being multistate, our CMs can account for different states of locomotion in VE namely: walking, but also running and sprinting. Being personified, our CMs can be adapted to avatars physiology such as to its size, weight or training status. They can then take into account avatars fatigue and recuperation for updating visual CMs accordingly. Last, our approach is adapted to the topography of the VE. Running over a strong positive slope would rapidly decrease the advance speed of the avatar, increase its energy loss, and eventually change the locomotion mode, influencing the visual feedback of the camera motions. Our new approach relies on a locomotion simulator partially inspired by human physiology and implemented for a real-time use in Desktop VR. We have conducted a series of experiments to evaluate the perception of our new CMs by naive participants. Results notably show that participants could discriminate and perceive transitions between the different locomotion modes, by relying exclusively on our CMs. They could also perceive some properties of the avatar being used and, overall, very well appreciated the new CMs techniques. Taken together, our results suggest that our new CMs could be introduced in Desktop VR applications involving first-person navigation, in order to enhance sensations of walking, running, and sprinting, with potentially different avatars and over uneven terrains, such as for: training, virtual visits or video games.