REGEN：基於雙階段生成網絡框架的遊戲實時寫實感增強技術

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

寫實感是現代電子遊戲的重要面向，因其能塑造玩家體驗，同時影響沉浸感、敘事參與度及視覺逼真度。儘管近期硬體技術的突破與尖端渲染技術已大幅提升遊戲的視覺真實感，但在動態環境中實現即時幀率的真實寫實仍是一大挑戰，這源於視覺品質與效能之間的權衡。本短文提出了一種新穎方法，利用生成對抗網絡來增強渲染遊戲畫面的寫實感。為此，我們提出了基於雙階段生成網絡框架的遊戲即時寫實增強技術（REGEN），該框架採用強大的無配對圖像到圖像轉換模型，生成語義一致的真實感畫面，將問題轉化為更簡單的配對圖像到圖像轉換任務。這使得我們能夠以輕量級方法進行訓練，實現即時推理而不犧牲視覺品質。我們在《俠盜獵車手V》上展示了該框架的有效性，結果顯示，該方法在視覺效果上與強大的無配對Im2Im方法相當，同時推理速度提升了32.14倍。研究還表明，相較於直接訓練輕量級無配對Im2Im轉換方法將遊戲畫面轉換為現實世界圖像視覺特徵所產生的寫實增強畫面，本方法結果更為優異。本工作的代碼、預訓練模型及演示可於以下網址獲取：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.