HairNet: Hairstyle Transfer with Pose Changes

We propose a novel algorithm for automatic hairstyle transfer, specifically targeting complicated inputs that do not match in pose. The input to our algorithm are two images, one for the hairstyle and one for the identity (face). We do not require any additional inputs such as segmentation masks. Our algorithm consists of multiple steps and we contribute three novel components. The first contribution is the idea to include baldification into hairstyle editing pipelines to simplify inpainting of background and face regions covered by hair. The second contribution is a novel embedding algorithm that can handle both pose changes and semantic image blending. The third contribution is the hairnet architecture that semantically blends the hairstyle and identity images, performing multiple tasks jointly, such as baldification of the identity image, transformation estimation between the two images, warping, and hairstyle copying. Our results show a clear improvement over current state of the art methods in both quantitative and qualitative results. Code and data will be released.