Neural 3D Face Shape Stylization Based on Single Style Template via Weakly Supervised Learning
Bibliographic record
Abstract
3D Face shape stylization refers to transforming a realistic 3D face shape into a different style, such as a cartoon face style. To solve this problem, this paper proposes modeling this task as a deformation transfer problem. This approach significantly reduces labor costs, as the artists would only need to create a single template for each face style. Realistic facial features of the original 3D face e.g. the nose or chin shape, would thus be automatically transferred to those in the style template. Deformation transfer methods, however, have two drawbacks. They are slow and they require re-optimization for every new input face. To address these weaknesses, we propose a neural network-based 3D face shape stylization method. This method is trained through weakly supervised learning, and its template's structure is preserved using our novel template-guided mesh smoothing regularization. Our method is the first learning-based deformation transfer method for 3D face shape stylization. Its employment offers the useful and practical benefit of not requiring paired training data. The experiments show that the quality of the stylized faces obtained by our method is comparable to that of the traditional deformation transfer method, achieving an average Chamfer Distance of approximately 0.01 mm. However, our approach significantly boosts the processing speed, achieving a rate approximately 3,000 times faster than the traditional deformation transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".