Hair-GANs: Recovering 3D Hair Structure from a Single Image

Understand

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.