2D Hair Strands Generation Based on Template Matching
Bibliographic record
Abstract
Hair modelling is an important part of many applications in computer graphics. Since 2D hair strands represent the information of the hair shape and the feature of the hairstyles, the generation of 2D hair strands is an essential part for image-based hair modelling. In this paper, we present a novel algorithm to generate 2D hair strands based on a template matching method. The method first divides a real hairstyle input image into sub-images with the predefined size. For each sub-image, an orientation map is estimated using Gabor filter and the orientation feature is presented by the orientation histogram. Then it matches the orientation histograms between each sub-image and template images in our database. Based on the matching results, the sub-images are replaced by the corresponding manual stroke images to give a clear representation of 2D hair strands. The result is refined by connecting the strands between adjacent sub-images. Finally, based on the control points defined on the 2D hair strands, the spline representation is applied to obtain smooth hair strands. Experimental results indicate that our algorithm is feasible.
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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".