Lightweight, Edge-Aware, and Temporally Consistent Supersampling for Mobile Real-Time Rendering
Yang, Sipeng Ji, Jiayu Zhuge, Junhao Zhao, Jinzhe Qiu, Qiang Li, Chen Yan, Yuzhong Wang, Kerong Yan, Lingqi Jin, Xiaogang
Yang, Sipeng
Ji, Jiayu
Zhuge, Junhao
Zhao, Jinzhe
Qiu, Qiang
Li, Chen
Yan, Yuzhong
Wang, Kerong
Yan, Lingqi
Jin, Xiaogang
Supervisor
Department
Computer Science
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Supersampling has proven highly effective in enhancing visual fidelity by reducing aliasing, increasing resolution, and generating interpolated frames. It has become a standard component of modern real-time rendering pipelines. However, on mobile platforms, deep learning-based supersampling methods remain impractical due to stringent hardware constraints, while non-neural supersampling techniques often fall short in delivering perceptually high-quality results. In particular, producing visually pleasing reconstructions and temporally coherent interpolations is still a significant challenge in mobile settings. In this work, we present a novel, lightweight supersampling framework tailored for mobile devices. Our approach substantially improves both image reconstruction quality and temporal consistency while maintaining real-time performance. For super-resolution, we propose an intra-pixel object coverage estimation method for reconstructing high-quality anti-aliased pixels in edge regions, a gradient-guided strategy for non-edge areas, and a temporal sample accumulation approach to improve overall image quality. For frame interpolation, we develop an efficient motion estimation module coupled with a lightweight fusion scheme that integrates both estimated optical flow and rendered motion vectors, enabling temporally coherent interpolation of object dynamics and lighting variations. Extensive experiments demonstrate that our method consistently outperforms existing baselines in both perceptual image quality and temporal smoothness, while maintaining real-time performance on mobile GPUs. A demo application and supplementary materials are available on the project page.
Citation
YangSipeng et al., “Lightweight, Edge-Aware, and Temporally Consistent Supersampling for Mobile Real-Time Rendering,” ACM Transactions on Graphics (TOG), vol. 44, no. 6, pp. 1–12, Dec. 2025, doi: 10.1145/3763348
Source
ACM Transactions on Graphics
Conference
Keywords
Supersampling, Mobile Rendering, Image Reconstruction, Frame Interpolation, Real-Time Graphics
Subjects
Source
Publisher
Association for Computing Machinery