We present Neural Strands, a novel learning framework for modeling accurate
hair geometry and appearance from multi-view image inputs. The learned hair
model can be rendered in real-time from any viewpoint with high-fidelity
view-dependent effects. Our model achieves intuitive shape and style control
unlike volumetric counterparts. To enable these properties, we propose a novel
hair representation based on a neural scalp texture that encodes the geometry
and appearance of individual strands at each texel location. Furthermore, we
introduce a novel neural rendering framework based on rasterization of the
learned hair strands. Our neural rendering is strand-accurate and anti-aliased,
making the rendering view-consistent and photorealistic. Combining appearance
with a multi-view geometric prior, we enable, for the first time, the joint
learning of appearance and explicit hair geometry from a multi-view setup. We
demonstrate the efficacy of our approach in terms of fidelity and efficiency
for various hairstyles.

本文提出了一种名为 Neural Strands 的新型学习框架，它可以从多视角图像输入中准确地建模头发的几何形状和外观，并提出了基于神经头皮纹理的新型头发表示和基于光栅化的新型神经渲染框架，可以同时实现外观和外显头发几何学的联合学习。