We introduce a deep learning-based method to generate full 3D hair geometry
from an unconstrained image. Our method can recover local strand details and
has real-time performance. State-of-the-art hair modeling techniques rely on
large hairstyle collections for nearest neighbor retrieval and then perform
ad-hoc refinement. Our deep learning approach, in contrast, is highly efficient
in storage and can run 1000 times faster while generating hair with 30K
strands. The convolutional neural network takes the 2D orientation field of a
hair image as input and generates strand features that are evenly distributed
on the parameterized 2D scalp. We introduce a collision loss to synthesize more
plausible hairstyles, and the visibility of each strand is also used as a
weight term to improve the reconstruction accuracy. The encoder-decoder
architecture of our network naturally provides a compact and continuous
representation for hairstyles, which allows us to interpolate naturally between
hairstyles. We use a large set of rendered synthetic hair models to train our
network. Our method scales to real images because an intermediate 2D
orientation field, automatically calculated from the real image, factors out
the difference between synthetic and real hairs. We demonstrate the
effectiveness and robustness of our method on a wide range of challenging real
Internet pictures and show reconstructed hair sequences from videos.

使用深度学习方法生成三维头发几何结构，性能实时，能恢复局部细节，并引入碰撞损失以合成更合理的发型。