We present User-predictable Face Editing (UP-FacE) -- a novel method for predictable face shape editing. In stark contrast to existing methods for face editing using trial and error, edits with UP-FacE are predictable by the human user. That is, users can control the desired degree of change precisely and deterministically and know upfront the amount of change required to achieve a certain editing result. Our method leverages facial landmarks to precisely measure facial feature values, facilitating the training of UP-FacE without manually annotated attribute labels. At the core of UP-FacE is a transformer-based network that takes as input a latent vector from a pre-trained generative model and a facial feature embedding, and predicts a suitable manipulation vector. To enable user-predictable editing, a scaling layer adjusts the manipulation vector to achieve the precise desired degree of change. To ensure that the desired feature is manipulated towards the target value without altering uncorrelated features, we further introduce a novel semantic face feature loss. Qualitative and quantitative results demonstrate that UP-FacE enables precise and fine-grained control over 23 face shape features.

翻译：我们提出了用户可预测的人脸编辑（UP-FacE）——一种用于可预测人脸形状编辑的新方法。与现有通过试错进行人脸编辑的方法形成鲜明对比，UP-FacE的编辑结果对人类用户而言是可预测的。也就是说，用户可以精确且确定地控制期望的改变程度，并能预先知晓实现特定编辑效果所需的改变量。我们的方法利用面部关键点来精确测量面部特征值，从而无需手动标注属性标签即可训练UP-FacE。UP-FacE的核心是一个基于Transformer的网络，它以来自预训练生成模型的潜在向量和面部特征嵌入作为输入，并预测一个合适的操纵向量。为了实现用户可预测的编辑，一个缩放层会调整操纵向量以达到精确期望的改变程度。为了确保所需特征被操纵至目标值而不改变无关特征，我们进一步引入了一种新颖的语义人脸特征损失函数。定性和定量结果表明，UP-FacE能够对23个人脸形状特征实现精确且细粒度的控制。