UP-FacE: User-predictable Fine-grained Face Shape Editing

Florian Strohm, Mihai Bâce, Andreas Bulling

Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2026.

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Abstract

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. Additionally, we analyse the correlations between different facial features and incorporate this information into the transformer network’s regularization process. This dynamic regularization allows naturally correlated facial features to be adjusted simultaneously in a coherent manner. Qualitative and quantitative results demonstrate that UP-FacE provides competitive face editing capabilities while offering precise and user-predictable control over 23 distinct facial shape features.

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