  Facial action unit (AU) intensity plays a pivotal role in quantifying
fine-grained expression behaviors, which is an effective condition for facial
expression manipulation. However, publicly available datasets containing
intensity annotations for multiple AUs remain severely limited, often featuring
a restricted number of subjects. This limitation places challenges to the AU
intensity manipulation in images due to disentanglement issues, leading
researchers to resort to other large datasets with pretrained AU intensity
estimators for pseudo labels. In addressing this constraint and fully
leveraging manual annotations of AU intensities for precise manipulation, we
introduce AUEditNet. Our proposed model achieves impressive intensity
manipulation across 12 AUs, trained effectively with only 18 subjects.
Utilizing a dual-branch architecture, our approach achieves comprehensive
disentanglement of facial attributes and identity without necessitating
additional loss functions or implementing with large batch sizes. This approach
offers a potential solution to achieve desired facial attribute editing despite
the dataset's limited subject count. Our experiments demonstrate AUEditNet's
superior accuracy in editing AU intensities, affirming its capability in
disentangling facial attributes and identity within a limited subject pool.
AUEditNet allows conditioning by either intensity values or target images,
eliminating the need for constructing AU combinations for specific facial
expression synthesis. Moreover, AU intensity estimation, as a downstream task,
validates the consistency between real and edited images, confirming the
effectiveness of our proposed AU intensity manipulation method.
