Neural Super-Resolution for Real-time Rendering with Radiance Demodulation
CVPR 2024(2023)
摘要
It is time-consuming to render high-resolution images in applications such as
video games and virtual reality, and thus super-resolution technologies become
increasingly popular for real-time rendering. However, it is challenging to
preserve sharp texture details, keep the temporal stability and avoid the
ghosting artifacts in real-time super-resolution rendering. To address this
issue, we introduce radiance demodulation to separate the rendered image or
radiance into a lighting component and a material component, considering the
fact that the light component is smoother than the rendered image so that the
high-resolution material component with detailed textures can be easily
obtained. We perform the super-resolution on the lighting component only and
re-modulate it with the high-resolution material component to obtain the final
super-resolution image with more texture details. A reliable warping module is
proposed by explicitly marking the occluded regions to avoid the ghosting
artifacts. To further enhance the temporal stability, we design a
frame-recurrent neural network and a temporal loss to aggregate the previous
and current frames, which can better capture the spatial-temporal consistency
among reconstructed frames. As a result, our method is able to produce
temporally stable results in real-time rendering with high-quality details,
even in the challenging 4 × 4 super-resolution scenarios.
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