# Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

arXiv: https://arxiv.org/abs/2308.06699 · HTML: https://mlsanity.com/paper/2308.06699/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2023.

## Built on by (most cited first, 4 of 4)

- [Wavelet-Space Representations for Neural Super-Resolution in Rendering Pipelines](https://mlsanity.com/paper/2508.16024/) (2025)
- [3DGS3: Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering](https://mlsanity.com/paper/2605.11489/) (2026)
- [Revisiting the Current Frame: Physical-Trace-Guided Network Output Correction for Video Restoration](https://mlsanity.com/paper/2608.09342/) (2026)
- [Frozen History: When Temporal Losses Stop Accumulation in Learned Real-Time Supersampling](https://mlsanity.com/paper/2610.15752/) (2026)

## Abstract (arXiv)

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.