StyleMorpheus: Learning a StyleGAN-based 3D-aware morphable face model with a disentangled style space
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".