Towards Interpretable Face Morphing via Unsupervised Learning of Layer‐Wise and Local Features

Discovering meaningful face morphing is critical for applications in image synthesis. Traditional unsupervised methods rely on global or layer‐wise representations, neglecting finer local details and thus limiting the control over specific facial attributes. In this work, we introduce an improved unsupervised approach that leverages contrastive learning and K‐means clustering to learn both layer‐wise and local features (LLF) in the latent space of StyleGAN. Our method segments latent representations into multiple local components across different layers, enabling fine‐grained control over attributes such as hair, eyes, and mouth. Experimental results demonstrate that LLF outperforms existing methods by providing more interpretable facial transformations while preserving high image realism, offering a promising solution for enhanced unsupervised face morphing applications. The code is available at https://github.com/disanda/LLF .