Rendering realistic images in real-time on high-frame-rate display devices poses considerable challenges, even with advanced graphics cards. This stimulates a demand for frame prediction technologies to boost frame rates. The key to these algorithms is to exploit spatiotemporal coherence by warping rendered pixels with motion representations. However, existing motion estimation methods can suffer from low precision, high overhead, and incomplete support for visual effects. In this article, we present a rendered frame prediction framework with a novel motion representation, dubbed motion-guided flow (MoFlow) , aiming at overcoming the intrinsic limitations of optical flow and motion vectors and precisely capture the dynamics of intricate geometries, lighting, and translucent objects. Notably, we construct MoFlows using a recurrent feature streaming network, which specializes in learning latent motion features from multiple frames. The results of extensive experiments demonstrate that, compared to state-of-the-art methods, our method achieves superior visual quality and temporal stability with lower latency. The recurrent mechanism allows our method to predict single or multiple consecutive frames, increasing the frame rate by over 2×. The proposed approach represents a flexible pipeline to meet the demands of various graphics applications, devices, and scenarios.

The utilization of dedicated ray tracing graphics cards has revolutionized the production of stunning visual effects in real-time rendering. However, the demand for high frame rates and high resolutions remains a challenge. The pixel warping approach is a crucial technique for increasing frame rate and resolution by exploiting the spatio-temporal coherence. To this end, existing super-resolution and frame prediction methods rely heavily on motion vectors from rendering engine pipelines to track object movements. This work builds upon state-of-the-art heuristic approaches by exploring a novel adaptive recurrent frame prediction framework that integrates learnable motion vectors. Our framework supports the prediction of transparency, particles, and texture animations, with improved motion vectors that capture shading, reflections, and occlusions, in addition to geometry movements. In addition, we introduce a feature streaming neural network, dubbed FSNet, that allows for the adaptive prediction of one or multiple sequential frames. Extensive experiments against state-of-the-art methods demonstrate that FSNet can operate at lower latency with significant visual enhancements and can upscale frame rates by at least two times. This approach offers a flexible pipeline to improve the rendering frame rates of various graphics applications and devices.

As an extension of the Internet of things (IoT), Internet of vehicles (IoV) paradigm plays a crucial role in advancing the development of smart cities. IoV relies on vehicular communication, enabling real-time interactions between vehicles, roadside infrastructure, and pedestrians. The main goal of IoV is not only to enhance road safety services but also to support time-sensitive IoT applications. In this context, cellular vehicle-to-everything (C-V2X) communication emerges as a prominent technology for achieving IoV goals. However, C-V2X communication system faces great challenges due to spectrum scarcity and the requirement for low-latency communication in high mobility and dynamic channel conditions. To meet these challenges, cognitive radio (CR)-inspired nonorthogonal multiple access (NOMA) has emerged as a promising solution to enhance the system capacity and spectrum efficiency. In this paper, we propose a novel CR paradigm, which utilizes the spectrum holes in an embedded mode. Namely, the secondary users (SUs) are allowed to access the holes released by idle primary users (PUs) without degrading the performance of active PUs. In addition, considering the channel aging phenomenon, we perform channel prediction to reduce the performance degradation. An online learning-based scheme that enables real-time resource allocation within the embedded CR assisted NOMA framework is then designed. Simulation results demonstrate superior performance gain of the proposed scheme. When compared with the conventional NOMA, the assistance of CR brings around threefold capacity gain, and when compared with the random allocation scheme, the capacity is increased by 21%.