Comparing Segmentation Approaches for Learning-Aware Wireframe Generation on Human Model
Bibliographic record
Abstract
Abstract Wireframe has been proved very useful for learning human body from semantic parameters. However, the definition of the wireframe is highly dependent on the anthropological experiences of experts in previous works. Hence it is usually not easy to obtain a well-defined wireframe for a new set of human models in the available database. To overcome such difficulty, an automated wireframe generation method would be very helpful in relieving the need for manual anthropometric definition. In order to find such an automated wireframe designing method, a natural way is using automatic segmentation methods to divide the human body model into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in various wireframes. How these wireframes affect human body learning performance? In this paper, we attempt to answer this research question by comparing different segmentation methods. Different wireframes are generated with the mesh segmentation methods, and then we use these wireframes as an intermediate agent to learn the relationship between the human body mesh models and the semantic parameters. We compared the reconstruction accuracy with different generated wireframe sets and summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".