用神经网络实时修复低采样发丝图像，提升渲染质量。
Real-Time Neural Hair G-Buffer Anti-Aliasing
- 先用神经网络恢复像素内发丝可见度和切线方向
- 再根据切线引导补全发丝位置，实现物理渲染
- 支持各种发型动态场景，效果优于DLSS、FSR
我们提出一种轻量级实时方法，从严重欠采样的光栅化输入中重建基于线段的发丝G-Buffers。该流程首先通过神经空间重建与时间累积恢复发丝覆盖率（即像素内的发丝可见度）及其切线方向，随后利用切线引导重建发丝位置，并用于物理基础的延迟发丝着色。我们在多种发型（直发、波浪发、爆炸头、马尾）及静态与动态场景下评估了本方法，结果表明其在发丝重建质量上优于通用工业级神经重建方案（如DLSS、FSR）。
原文摘要 · Abstract (English)
We propose a lightweight real-time method for reconstructing strand-based hair G-Buffers from severely undersampled rasterized inputs. Our pipeline first applies neural spatial reconstruction and temporal accumulation to recover hair coverage, i.e., fractional hair visibility within a pixel, and tangent. It then uses a tangent-guided reconstruction step to complete the position, which is subsequently used for physically based deferred hair shading. We evaluate our method across a diverse set of hairstyles, including straight, wavy, afro, and ponytail styles, under both static and dynamic scenarios. Our method achieves higher hair reconstruction quality than general industrial neural reconstruction solutions such as DLSS and FSR.
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