Neural Outline Cache for Real-time Anti-aliasing Font Rendering
Jiashuaizi Mo, Sang-Woon Jeon, Hua Wang, Xiangqi Chen, Yanchao Wang, Minglu Li, Zhonglong Zheng
Abstract
Neural textures have emerged as pivotal assets in nextgeneration neural rendering pipelines. However, hardware limitations and programming interface constraints lead to suboptimal performance in multi-instance real-time rendering scenarios. This bottleneck becomes particularly acute for texture-intensive tasks such as font rendering. To address this, we propose Neural Outline Cache (NOC), a novel neural font texture supporting real-time anti-aliased rendering and procedural editing within modern neural graphics pipelines. NOC's lightweight network leverages multi-resolution hash encoding to cache spline-derived SDFs, delivering anti-aliased rendering via standard graphics pipelines. For massive-instance scalability, our cache buffer layout (CBL) and batch-fused inference (BFI), tailored for NOC, mitigate neural texture streaming bottlenecks. We constructed an evaluation dataset using five font styles. In offline rendering, our proposed method achieves overall average results of 57.35 dB PSNR, 0.998 SSIM, and 1.1584e-3 pixel RMSE, while maintaining approximately 0.5ms frame latency with 500 real-time instances. To demonstrate its versatility, we integrated a procedural editor for visual effects editing of NOC textures. These results all prove that NOC is a reliable, production-ready neural asset.
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