RenderGAN: Enhancing Real-Time Rendering Efficiency with Deep Learning

In the domain of computer graphics, achieving high visual quality in real-time rendering remains a formidable challenge due to the inherent time-quality trade-off. Conventional real-time rendering engines sacrifice visual fidelity for interactive performance, while image generation using path-tracing techniques can be exceedingly time-consuming. In this paper, we introduce RenderGAN, a deep learning-based solution designed to address this critical challenge in real-time rendering. RenderGAN uses G-Buffers and information from a real-time rendering engine as inputs to produce output images with exceptional visual fidelity. Its encoder-decoder architecture, trained using the Generative Adversarial Network (GAN) framework with perceptual loss, enhances image realism. To evaluate RenderGAN's effectiveness, we quantitatively compare the generated images with those of a path-tracing engine, obtaining a remarkable Universal Image Quality Index (UIQI) value of 0.898. RenderGAN's open-source nature fosters collaboration, driving advancements in real-time computer graphics and rendering techniques. By bridging the gap between real-time and path-tracing rendering, RenderGAN opens new horizons for accelerated image generation, inspiring innovation and unlocking the full potential of real-time visual experiences. Project page: https://github.com/marcomameli1992/RenderNet