Segmentation-Based Wireframe Generation for Parametric Modeling of Human Body Shapes
Bibliographic record
Abstract
Abstract Wireframes have been proved useful as an intermediate layer of the neural network to learn the relationship between the human body and semantic parameters. However, the definition of the wireframe needs to have anthropological meaning and is highly dependent on experts’ experience. Hence, it is usually not easy to obtain a well-defined wireframe for a new set of shapes in available databases. An automated wireframe generation method would help relieve the need for the manual anthropometric definition to overcome such difficulty. One way to find such an automated wireframe generation method is to apply segmentation to divide the models into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in diversified wireframes. How do these different sets of wireframes affect learning performance? In this paper, we attempt to answer this research question by defining several critical quantitative estimators to evaluate different wireframes’ learning performance. To find how such estimators influence wireframe-assisted learning accuracy, we conduct experiments by comparing different segmentation methods on human body shapes. We summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning with such verification.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".