GameIR: A Large-Scale Synthesized Ground-Truth Dataset for Image Restoration over Gaming Content
Lebin Zhou, Kun Han, Nam Ling, Wei Wang, Wei Jiang

TL;DR
GameIR provides a large-scale, high-quality synthesized dataset specifically designed for image restoration tasks like super-resolution and novel view synthesis in gaming content, addressing the lack of suitable ground-truth data.
Contribution
We introduce GameIR, a comprehensive synthetic dataset with paired ground-truth images and auxiliary rendering buffers, tailored for improving restoration methods in gaming applications.
Findings
SOTA super-resolution algorithms show improved performance on GameIR data.
Incorporating GBuffer information enhances restoration quality.
Dataset and models are publicly released for research advancement.
Abstract
Image restoration methods like super-resolution and image synthesis have been successfully used in commercial cloud gaming products like NVIDIA's DLSS. However, restoration over gaming content is not well studied by the general public. The discrepancy is mainly caused by the lack of ground-truth gaming training data that match the test cases. Due to the unique characteristics of gaming content, the common approach of generating pseudo training data by degrading the original HR images results in inferior restoration performance. In this work, we develop GameIR, a large-scale high-quality computer-synthesized ground-truth dataset to fill in the blanks, targeting at two different applications. The first is super-resolution with deferred rendering, to support the gaming solution of rendering and transferring LR images only and restoring HR images on the client side. We provide 19200 LR-HR…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Generative Adversarial Networks and Image Synthesis · Video Analysis and Summarization
