  Despite the recent success of face image generation with GANs, conditional
hair editing remains challenging due to the under-explored complexity of its
geometry and appearance. In this paper, we present MichiGAN
(Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation
method for interactive portrait hair manipulation. To provide user control over
every major hair visual factor, we explicitly disentangle hair into four
orthogonal attributes, including shape, structure, appearance, and background.
For each of them, we design a corresponding condition module to represent,
process, and convert user inputs, and modulate the image generation pipeline in
ways that respect the natures of different visual attributes. All these
condition modules are integrated with the backbone generator to form the final
end-to-end network, which allows fully-conditioned hair generation from
multiple user inputs. Upon it, we also build an interactive portrait hair
editing system that enables straightforward manipulation of hair by projecting
intuitive and high-level user inputs such as painted masks, guiding strokes, or
reference photos to well-defined condition representations. Through extensive
experiments and evaluations, we demonstrate the superiority of our method
regarding both result quality and user controllability. The code is available
at https://github.com/tzt101/MichiGAN.
