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.

中文翻译：

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GFFE：用于低延迟实时渲染的无 G 缓冲区帧外推


实时渲染一直采用要求越来越高的效果，例如光线追踪。但是，以高分辨率和高帧速率渲染此类效果仍然具有挑战性。与 DLSS 3 和 FSR 3 等帧插值方法相比，帧外插方法不会引入额外的延迟，它通过基于前一帧生成未来帧来提高帧速率。然而，由于消遮挡区域缺乏信息和复杂的未来运动，这是一项更具挑战性的任务，而且由于需要 G 缓冲区作为输入，最近的方法也具有很高的引擎集成成本。我们提出了一种无 G 缓冲区的帧外推方法 GFFE，它具有新颖的启发式框架和高效的神经网络，可以合理地实时生成新帧，而不会引入额外的延迟。我们分析了动态片段的运动和不同类型的消遮挡，并设计了外推块的相应模块来处理它们。之后，使用轻量级阴影校正网络来校正阴影并提高整体质量。GFFE 获得的结果与以前的插值和 G 缓冲区依赖型外推方法相当或更好，性能更高效，集成更容易。