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 existing hair-specific denoising techniques and general industrial neural reconstruction solutions such as DLSS and FSR.

翻译：我们提出一种轻量级实时方法，用于从严重欠采样的光栅化输入中重建基于发丝的毛发G-Buffer。该流程首先应用神经空间重建与时间累积来恢复毛发覆盖率（即像素内毛发的部分可见性）及切线，随后采用切线引导的重建步骤补全位置信息，并最终用于基于物理的延迟毛发着色。我们在包括直发、波浪卷发、爆炸头及马尾辫等多种发型（涵盖静态与动态场景）中评估了该方法。实验表明，相较于现有毛发专用去噪技术及通用工业级神经重建方案（如DLSS和FSR），本方法可实现更高的毛发重建质量。