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。

神经串：从多视图图像中学习头发的几何和外貌

Neural Strands: Learning Hair Geometry and Appearance from Multi-View  Images

We present a novel Relightable Neural Renderer (RNR) for simultaneous view
synthesis and relighting using multi-view image inputs. Existing neural
rendering (NR) does not explicitly model the physical rendering process and
hence has limited capabilities on relighting. RNR instead models image
formation in terms of environment lighting, object intrinsic attributes, and
light transport function (LTF), each corresponding to a learnable component. In
particular, the incorporation of a physically based rendering process not only
enables relighting but also improves the quality of view synthesis.
Comprehensive experiments on synthetic and real data show that RNR provides a
practical and effective solution for conducting free-viewpoint relighting.

本文提出了一种新颖的可重光的神经渲染器（RNR），可以使用多视图图像输入进行同时视图合成和重光。与现有的神经渲染（NR）不同，RNR 明确建模物理渲染过程，从而提高了调光的能力，并提高了视图合成的质量。对合成和真实数据的全面实验表明，RNR 为进行自由视点重光提供了实用有效的解决方案。