Real-Time Neural Hair Denoising

TL;DR

This paper introduces a lightweight, real-time neural method for reconstructing detailed strand-based hair G-Buffers from severely undersampled inputs, improving quality over existing techniques.

Contribution

It presents a novel neural pipeline combining spatial reconstruction, temporal accumulation, and tangent-guided completion for high-quality, real-time hair rendering.

Findings

Achieves higher hair reconstruction quality than existing denoising techniques.

Effective across diverse hairstyles and dynamic scenarios.

Outperforms industrial neural reconstruction solutions like DLSS and FSR.

Abstract

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.