Neural Strands: Learning Hair Geometry
and Appearance from Multi-View Images
ECCV 2022
Abstract
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.
Video
![](img/overview/test_crop.png)
Editability
Our system recovers strand-based geometry together with appearance. This explicit geometry allows for direct editing of the hair and creation of hair-in-wind effects or a virtual haircut. This is in strong contrast to volumetric approaches which do not allow modification of the hair geometry.
![](img/animation/pred_composed_without_bg0.png)
![](img/animation/pred_composed_without_bg1.png)
![](img/animation/pred_composed_without_bg3.png)
![](img/grow/8.png)
Comparison
NeRF and MVP(Mixture of Volumetric Primitives) fail to recover high frequency detail on the hair. Our method recovers more detailed hair together with strand geometry even for stray-away hairs.
![NeRF](img/comparison/woman_bangs_2/nerf_crop.png)
![MVP](img/comparison/woman_bangs_2/mvp_crop.png)
![Ours](img/comparison/woman_bangs_2/ours_crop.png)
Citation
Acknowledgements
The website template was borrowed from Michaël Gharbi.