Real-Time Neural Hair Denoising 事件

Real-Time Neural Hair Denoising arXiv:2605.17557v2 Announce Type: replace-cross 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