HairNeRF: Geometry-Aware Image Synthesis for Hairstyle Transfer

Abstract

We propose a novel hairstyle transferred image synthesis method considering the underlying head geometry of two input images. In traditional GAN-based methods, transferring hairstyle from one image to the other often makes the synthesized result awkward due to differences in pose, shape, and size of heads. To resolve this, we utilize neural rendering by registering two input heads in the volumetric space to make a transferred hairstyle fit on the head of a target image. Because of the geometric nature of neural rendering, our method can render view varying images of synthesized results from a single transfer process without causing distortion from which extant hairstyle transfer methods built upon traditional GAN-based generators suffer. We verify that our method surpasses other baselines in view of pre-serving the identity and hairstyle of two input images when synthesizing a hairstyle transferred image rendered at any point of view.