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Handheld Augmented Reality commonly implements some variant of magic lensrendering, which turns only a fraction of the user's real environment into ARwhile the rest of the environment remains unaffected. Since handheld AR devicesare commonly equipped with video see-through capabilities, AR magic lensapplications often suffer from spatial distortions, because the AR environmentis presented from the perspective of the camera of the mobile device. Recentapproaches counteract this distortion based on estimations of the user's headposition, rendering the scene from the user's perspective. To this end,approaches usually apply face-tracking algorithms on the front camera of themobile device. However, this demands high computational resources and thereforecommonly affects the performance of the application beyond the already highcomputational load of AR applications. In this paper, we present a method toreduce the computational demands for user perspective rendering by applyinglightweight optical flow tracking and an estimation of the user's motion beforehead tracking is started. We demonstrate the suitability of our approach forcomputationally limited mobile devices and we compare it to device perspectiverendering, to head tracked user perspective rendering, as well as to fixedpoint of view user perspective rendering.

Adaptive User Perspective Rendering for Handheld Augmented Reality