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.

我们提出了 AUEditNet 模型，通过使用双分支架构，在仅有 18 个被试者的情况下实现了对 12 个面部动作单元（AU）的印象深刻的强度操纵。该方法在面部属性和身份方面实现了全面的解耦，无需额外的损失函数或大批量训练，为在数据集主体数量有限的情况下实现期望的面部属性编辑提供了潜在解决方案，并证实了 AUEditNet 在编辑 AU 强度方面的高准确性，验证了我们提出的 AU 强度操纵方法的有效性。