Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which do not introduce additional latency as opposed to frame interpolation methods such as DLSS 3 and FSR 3, boost the frame rate by generating future frames based on previous frames. However, it is a more challenging task because of the lack of information in the disocclusion regions and complex future motions, and recent methods also have a high engine integration cost due to requiring G-buffers as input. We propose a G-buffer free frame extrapolation method, GFFE, with a novel heuristic framework and an efficient neural network, to plausibly generate new frames in real time without introducing additional latency. We analyze the motion of dynamic fragments and different types of disocclusions, and design the corresponding modules of the extrapolation block to handle them. After that, a light-weight shading correction network is used to correct shading and improve overall quality. GFFE achieves comparable or better results than previous interpolation and G-buffer dependent extrapolation methods, with more efficient performance and easier integration.

We introduce ExtraSS, a novel framework that combines spatial super sampling and frame extrapolation to enhance real-time rendering performance. By integrating these techniques, our approach achieves a balance between performance and quality, generating temporally stable and high-quality, high-resolution results. Leveraging lightweight modules on warping and the ExtraSSNet for refinement, we exploit spatial-temporal information, improve rendering sharpness, handle moving shadings accurately, and generate temporally stable results. Computational costs are significantly reduced compared to traditional rendering methods, enabling higher frame rates and alias-free high resolution results. Evaluation using Unreal Engine demonstrates the benefits of our framework over conventional individual spatial or temporal super sampling methods, delivering improved rendering speed and visual quality. With its ability to generate temporally stable high-quality results, our framework creates new possibilities for real-time rendering applications, advancing the boundaries of performance and photo-realistic rendering in various domains.

Recent studies have shown that a non-negligible number of packets face multiple congested links on Internet paths. We investigate the impact of using multiple active queue management (AQM) routers on paths that consist of multiple congested links. We present the results of an ns-2 evaluation study of various AQM techniques that, in contrast to previous studies of AQM, uses a complex network topology including up to three congested links, reverse path traffic, and realistic round-trip times. We consider the effects of multiple AQM routers coupled with explicit congestion notification (ECN) on the throughput of long-lived flows and response times of web traffic. We find that, especially for web traffic, performance is improved as the number of AQM routers is increased, but that significant improvements occur with even a single AQM router.

Navigation in Virtual Environments (VEs) requires users to develop spatial knowledge of the environment primarily through visual cues provided to the user. Thus, the design and display of visual navigation cues is important for efficient navigation in a VE. In this paper, we report the results of an experiment in which three different visual cues were tested for their benefit toward users' navigation in a 3D virtual maze. The experiment varied the form of visual cue: a 2D map, a 2D map with a directionally ambiguous cue, and a 2D map with a directional cue. Eye tracking data was collected and analyzed to examine the correlation between the type of visual cue presented and the navigational efficiency of the user through the virtual maze. It was observed that the cue type affected performance of the participant in the 3D maze. The directional cue was most effective in the time taken by users to reach the center of the maze. Results of this study have implications for VE design as well as for game development.