Real-Time Neural Hair G-Buffer Anti-Aliasing
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 recon...
| Authors | Chenghao Wu, Yuefan Shen, Tao Huang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 17 May 2026 |
| Last updated at source | 31 August 2026 |
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