Perceptually-Guided VR Style Transfer

Abstract

Virtual reality (VR) makes it possible to provide immersive multimedia content composed of omnidirectional videos (ODVs). Towards enabling more immersive and satisfying VR content, methods are needed to manipulate VR scenes, taking into account perceptual factors related to viewers’ quality of experience (QoE). For example, style transfer methods can be applied to VR content, allowing users to create artistic or surreal effects in their immersive environments. Here, we study perceptual factors that affect the sensation of stylized immersiveness, including color dynamics and spatio-temporal consistency. To do this, we introduce an immersiveness sensitivity model of luminance and color perception, and use it to measure the color dynamics and spatio-temporal consistency of stylized VR contents. We subsequently use this model to construct a perceptually-guided VR style transfer model called VR Style Transfer GAN (VRST-GAN). VRST-GAN learns to transfer a desired style into VR to enhance immersiveness by considering color dynamics while preserving spatio-temporal consistency. We demonstrate the effectiveness of VRST-GAN via qualitative and quantitative experiments. We also develop a VR Immersiveness Predictor (VR-IP) that is able to predict the sensation of immersiveness using the perceptual model. In our experiments, VR-IP predicts immersiveness with an accuracy of 91%.