  Facial expression manipulation aims at editing facial expression with a given
condition. Previous methods edit an input image under the guidance of a
discrete emotion label or absolute condition (e.g., facial action units) to
possess the desired expression. However, these methods either suffer from
changing condition-irrelevant regions or are inefficient for fine-grained
editing. In this study, we take these two objectives into consideration and
propose a novel method. First, we replace continuous absolute condition with
relative condition, specifically, relative action units. With relative action
units, the generator learns to only transform regions of interest which are
specified by non-zero-valued relative AUs. Second, our generator is built on
U-Net but strengthened by Multi-Scale Feature Fusion (MSF) mechanism for
high-quality expression editing purposes. Extensive experiments on both
quantitative and qualitative evaluation demonstrate the improvements of our
proposed approach compared to the state-of-the-art expression editing methods.
Code is available at \url{https://github.com/junleen/Expression-manipulator}.
