Real-time rendering for video games has become increasingly challenging due to the need for higher resolutions, framerates and photorealism. Supersampling has emerged as an effective solution to address this challenge. Our work introduces a novel neural algorithm for supersampling rendered content that is 4 times more efficient than existing methods while maintaining the same level of accuracy. Additionally, we introduce a new dataset which provides auxiliary modalities such as motion vectors and depth generated using graphics rendering features like viewport jittering and mipmap biasing at different resolutions. We believe that this dataset fills a gap in the current dataset landscape and can serve as a valuable resource to help measure progress in the field and advance the state-of-the-art in super-resolution techniques for gaming content.

翻译：实时渲染在视频游戏中面临日益严峻的挑战，原因在于对更高分辨率、帧率及逼真度的需求。超采样技术已成为应对该挑战的有效解决方案。本研究提出一种新颖的神经网络算法，用于对渲染内容进行超采样，该算法在保持同等精度水平的同时，效率比现有方法提升4倍。此外，我们引入了一个全新数据集，该数据集通过视角抖动、Mipmap偏移等图形渲染特性，在不同分辨率下提供运动矢量和深度等辅助模态信息。我们认为，该数据集填补了当前数据集领域的空白，将作为宝贵资源帮助衡量该领域进展，并推动游戏内容超分辨率技术达到新水平。