  We introduce Hair-GANs, an architecture of generative adversarial networks,
to recover the 3D hair structure from a single image. The goal of our networks
is to build a parametric transformation from 2D hair maps to 3D hair structure.
The 3D hair structure is represented as a 3D volumetric field which encodes
both the occupancy and the orientation information of the hair strands. Given a
single hair image, we first align it with a bust model and extract a set of 2D
maps encoding the hair orientation information in 2D, along with the bust depth
map to feed into our Hair-GANs. With our generator network, we compute the 3D
volumetric field as the structure guidance for the final hair synthesis. The
modeling results not only resemble the hair in the input image but also
possesses many vivid details in other views. The efficacy of our method is
demonstrated by using a variety of hairstyles and comparing with the prior art.
