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：我们提出了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缓冲。我们的流程首先应用神经空间重建和时间累积来恢复毛发覆盖度（即像素内毛发的部分可见性）和切线，随后使用切线引导重建步骤完成位置信息的恢复，该位置信息最终用于基于物理的延迟毛发着色。我们在包括直发、卷发、爆炸头和马尾辫等多种发型上进行了静止与动态场景的评估。实验表明，本方法在毛发重建质量上优于现有的毛发专用去噪技术以及DLSS、FSR等通用工业级神经重建方案。