A Subspace-Based Method for Facial Image Editing

In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition subspace, enabling precise editing control over individual attributes with minimal impact on others and maintaining identity consistency. We introduce an adaptive weight modulation (AWM) method and a maximum slope truncation (MST) formula. The AWM method, founded on a sufficient convergent criterion, performs singular value decomposition to yield subspace parameters that preserve rich facial knowledge within the generative model, facilitating high-quality facial generation with reduced parameterization. This empowers meaningful semantic interpretation of attributes, supporting diverse editing tasks such as pose, age, and eyewear adjustments. The MST formula rigorously defines the editing bounds to effectively navigate the tradeoff between editing depth and identity retention. We also propose a guideline for deciphering the specific meanings of unsupervised semantics, potentially advancing interpretability in social behavioral studies. An accompanying web application, available at https://github.com/mickoluan/GreenLimeSia, has been developed, granting users the freedom to perform tailored facial edits. Extensive experimental results show we pave the way for more personalized and authentic interactions within computational social platforms.