Efficient neural supersampling on a novel gaming dataset
Antoine Mercier Ruan Erasmus Yashesh Savani Manik Dhingra Fatih Porikli Guillaume Berger
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.