StyleMorpheus: Learning a StyleGAN-based 3D-aware morphable face model with a disentangled style space
Peizhi Yan, Rabab Kreidieh Ward, Dan Wang, Qiang Tang, Shan Du · Neurocomputing · 2025
Recent advances in 3D-aware neural rendering have enabled photorealistic face image synthesis from arbitrary viewpoints. However, achieving disentangled control over facial attributes typically depends on large, curated datasets collected in controlled environments. To overcome this limitation, we introduce StyleMorpheus, a 3D-aware, StyleGAN-based morphable face model that can be trained entirely on in-the-wild face images. StyleMorpheus surpasses traditional 3D-aware morphable models in rendering quality, despite relying solely on unconstrained 2D training data. Unlike conventional StyleGAN-based methods, StyleMorpheus also provides disentangled control over facial identity, expression, and appearance, allowing each attribute to be adjusted independently without unintended changes to the others. StyleMorpheus employs an auto-encoder structure, where the encoder learns a representative, disentangled style code space, and the decoder enforces disentanglement by using shape- and appearance-related codes at different levels of the network. Furthermore, we fine-tune the decoder through StyleGAN-based generative adversarial learning to achieve photorealistic rendering quality. StyleMorpheus is computationally lightweight and achieves real-time rendering speeds, making it suitable for virtual reality applications. We further demonstrate the disentanglement capabilities of StyleMorpheus through face editing tasks such as style mixing, face morphing, and color editing. Project homepage: https://peizhiyan.github.io/docs/morpheus .