  The large discrepancy between the source non-makeup image and the reference
makeup image is one of the key challenges in makeup transfer. Conventional
approaches for makeup transfer either learn disentangled representation or
perform pixel-wise correspondence in a parametric way between two images. We
argue that non-parametric techniques have a high potential for addressing the
pose, expression, and occlusion discrepancies. To this end, this paper proposes
a \textbf{S}emi-\textbf{p}arametric \textbf{M}akeup \textbf{T}ransfer (SpMT)
method, which combines the reciprocal strengths of non-parametric and
parametric mechanisms. The non-parametric component is a novel
\textbf{S}emantic-\textbf{a}ware \textbf{C}orrespondence (SaC) module that
explicitly reconstructs content representation with makeup representation under
the strong constraint of component semantics. The reconstructed representation
is desired to preserve the spatial and identity information of the source image
while "wearing" the makeup of the reference image. The output image is
synthesized via a parametric decoder that draws on the reconstructed
representation. Extensive experiments demonstrate the superiority of our method
in terms of visual quality, robustness, and flexibility. Code and pre-trained
model are available at \url{https://github.com/AnonymScholar/SpMT.
