REGEN：通过双阶段生成网络框架实现游戏中的实时写实增强

REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework

August 23, 2025

作者: Stefanos Pasios, Nikos Nikolaidis

cs.AI

摘要

在当代视频游戏中，逼真度是一个至关重要的因素，它不仅塑造了玩家的体验，还深刻影响着沉浸感、叙事参与度以及视觉保真度。尽管近期硬件技术的突破与尖端渲染技术显著提升了游戏的视觉真实感，但在动态环境中以实时帧率实现真正的照片级真实感仍面临重大挑战，这主要源于视觉质量与性能之间的权衡。在这篇短文中，我们提出了一种利用生成对抗网络增强渲染游戏帧照片级真实感的新方法。为此，我们引入了基于双阶段生成网络框架的实时游戏照片级真实感增强技术（REGEN），该框架采用了一种鲁棒的无配对图像到图像转换模型，以生成语义一致的照片级真实帧，从而将问题简化为一个更简单的配对图像到图像转换任务。这使得我们能够采用轻量级方法进行训练，在不牺牲视觉质量的前提下实现实时推理。我们在《侠盗猎车手V》上验证了该框架的有效性，结果显示，该方法在视觉效果上与鲁棒的无配对Im2Im方法相当，同时推理速度提升了32.14倍。我们的研究还表明，相较于直接训练轻量级无配对Im2Im转换方法将游戏帧转换为现实世界图像视觉特征所得到的照片级真实感增强帧，REGEN的结果更为出色。本工作的代码、预训练模型及演示可在以下网址获取：https://github.com/stefanos50/REGEN。

English

Photorealism is an important aspect of modern video games since it can shape the player experience and simultaneously impact the immersion, narrative engagement, and visual fidelity. Although recent hardware technological breakthroughs, along with state-of-the-art rendering technologies, have significantly improved the visual realism of video games, achieving true photorealism in dynamic environments at real-time frame rates still remains a major challenge due to the tradeoff between visual quality and performance. In this short paper, we present a novel approach for enhancing the photorealism of rendered game frames using generative adversarial networks. To this end, we propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative Network framework (REGEN), which employs a robust unpaired image-to-image translation model to produce semantically consistent photorealistic frames that transform the problem into a simpler paired image-to-image translation task. This enables training with a lightweight method that can achieve real-time inference time without compromising visual quality. We demonstrate the effectiveness of our framework on Grand Theft Auto V, showing that the approach achieves visual results comparable to the ones produced by the robust unpaired Im2Im method while improving inference speed by 32.14 times. Our findings also indicate that the results outperform the photorealism-enhanced frames produced by directly training a lightweight unpaired Im2Im translation method to translate the video game frames towards the visual characteristics of real-world images. Code, pre-trained models, and demos for this work are available at: https://github.com/stefanos50/REGEN.