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The workload of real-time rendering is steeply increasing as the demand forhigh resolution, high refresh rates, and high realism rises, overwhelming mostgraphics cards. To mitigate this problem, one of the most popular solutions isto render images at a low resolution to reduce rendering overhead, and thenmanage to accurately upsample the low-resolution rendered image to the targetresolution, a.k.a. super-resolution techniques. Most existing methods focus onexploiting information from low-resolution inputs, such as historical frames.The absence of high frequency details in those LR inputs makes them hard torecover fine details in their high-resolution predictions. In this paper, wepropose an efficient and effective super-resolution method that predictshigh-quality upsampled reconstructions utilizing low-cost high-resolutionauxiliary G-Buffers as additional input. With LR images and HR G-buffers asinput, the network requires to align and fuse features at multi resolutionlevels. We introduce an efficient and effective H-Net architecture to solvethis problem and significantly reduce rendering overhead without noticeablequality deterioration. Experiments show that our method is able to producetemporally consistent reconstructions in $4 \times 4$ and even challenging $8\times 8$ upsampling cases at 4K resolution with real-time performance, withsubstantially improved quality and significant performance boost compared toexisting works.

FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion