Virtual reality (VR) is gaining increasing popularity across various domains, but the current state of technology imposes limitations on the level of realism and complexity achievable in computer graphics when displayed through VR head-mounted devices (HMDs). To improve the user experience in HMDs, optimization techniques are needed to enhance performance without sacrificing quality. One such technique is Foveated Rendering (FR), which leverages the human visual system to optimize resource usage. FR degrades the image quality at the periphery of the human vision, where visual acuity is lower, to save resources. This paper aims to investigate if the perception of the peripheral area is affected whenever users are in movement in a VR environment. Our findings show a significant correlation between speed movement and Foveated rendering parameters in both scenarios. The least amount of degradation was observed in the idle state and the most in the high-speed state, indicating that users perceive less degradation at higher speeds. These results are particularly relevant for path-tracing-based algorithms, due to the possibility of reducing the number of rays required for the rendering whenever there is movement.

Real time Path-tracing is becoming an important approach for the future of games, digital entertainment, and virtual reality applications that require realism and immersive environments. Among different possible optimizations, denoising Monte Carlo rendered images is necessary in low sampling densities. When dealing with Virtual Reality devices, other possibilities can also be considered, such as foveated rendering techniques. Hence, this work proposes a novel and promising rendering pipeline for denoising a real-time path-traced application in a dual-screen system such as head-mounted display (HMD) devices. Therefore, we leverage characteristics of the foveal vision by computing G-Buffers with the features of the scene and a buffer with the foveated distribution for both left and right screens. Later, we path trace the image within the coordinates buffer generating only a few initial rays per selected pixel, and reconstruct the noisy image output with a novel non-homogeneous denoiser that accounts for the pixel distribution. Our experiments showed that this proposed rendering pipeline could achieve a speedup factor up to 1.35 compared to one without our optimizations.