UP-FacE: User-predictable Fine-grained Face Shape Editing
This addresses the challenge of user control in face editing applications, offering a more deterministic approach for users in fields like digital media or entertainment, though it is incremental as it builds on pre-trained generative models.
The paper tackles the problem of unpredictable face shape editing in existing methods by introducing UP-FacE, which enables users to precisely control and predict editing outcomes, achieving fine-grained control over 23 face shape features with demonstrated qualitative and quantitative improvements.
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.