Efficient neural supersampling on a novel gaming dataset

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 super-sampling rendered content that is 4× 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.

1. Introduction

Real-time rendering has become increasingly difficult for video games due to the demand for higher resolutions, framerates and photorealism. One solution that has recently emerged to address this challenge consists in rendering at lower resolution and then use an upscaling technique to achieve the desired resolution. However, developing efficient upscaling solutions that balance speed and accuracy remains a challenge. Recently, several commercial solutions have been developed for gaming super-resolution, including those that are based on deep learning (DL) such as Nvidia’s DLSS [36] or Intel’s XeSS [11], as well as solutions that do not rely on machine learning, such as AMD’s FSR [18], [19]. Despite the availability of these commercial solutions, there has been relatively little published research on the application of DL-based super-resolution for gaming. We believe that one of the reasons why DL-based super-resolution for gaming has received little attention compared to super-resolution of natural content is that there is currently no standard, publicly available dataset for developing gaming-specific super-resolution solutions. Researchers and developers who want to study or improve upon existing methods must create their own datasets, which can be a time-consuming and resource-intensive process.