Customized 3D Clothes Modeling for Virtual Try-on System based on Multiple Kinects
Bibliographic record
Abstract
Most existing 3D virtual try-on systems put clothes designed in one environment on a human captured in another environment, which cause the mismatching color and brightness problem. And also typical 3D clothes modeling starts with manually designed 2D patterns, deforms them to fit on a human, and applies stitching to sew those patterns together. Such work usually relies on labor work. In this thesis, we describe an approach to reconstruct clothes and human that both are from the same space. With multiple Kinects, it models the 3D clothes directly out of a dressed mannequin without the need of the predefined 2D clothes patterns, and fits them to a human user. Our approach has several advantages: (i) a simple hardware setting consisted of multiple Kinects to capture a human model; (ii) 3D clothes modeling directly out of captured human figure. To the best of our knowledge, our work is the first one which separates clothes out of captured human figure; (iii) resizing of clothes adapting to any sized human user; (iv) a novel idea of virtual try-on where clothes and human are captured in the same location.
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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".