Single‐view procedural braided hair modeling through braid unit identification
Bibliographic record
Abstract
Abstract We propose the first approach that can generate procedural three‐dimensional (3D) hair involving braids modeled from a single‐view photograph. Existing single‐view image‐based hair modeling methods fail to handle braided hairstyles. Our approach combines image processing, deep neural networks, as well as two‐dimensional (2D) and 3D geometric algorithms. In order to train our neural network, we create a braid unit data set. Our recognition and segmentation system can successfully segment hair regions, braid and non‐braid regions, using convolutional neural networks. We further process the images to obtain the locations, sizes, and orientations of the braid units. Given these braid units, we perform braid structure analysis to obtain the braid strand curves. The procedural modeling of the 3D braids is represented using 3D helical curves where the parameters are extracted from the 2D image analysis. Furthermore, we extract 2D hair strands from the non‐braid region using the Gabor filter and orientation maps. Then, a 3D hair volume is generated with the hair region silhouette information. We project the 2D hair strands and braids on the 3D hair volume to obtain the 3D hair strands and 3D braids. The strands for the braid and non‐braid regions are used as guides to generate dense hair strands. Dense strands are emitted from the hair root triangle mesh and follow the guide strands. With a sparse set of landmarks, the hair region of the photograph is texture mapped to the 3D hair root mesh and used to color the strands. We successfully tested our approach on photographs showing variations of braid styles and hair color.
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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 | 0.001 | 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".