The Comparison of User Preference on Domestic versus a Foreign 3D Virtual Try-On System
Bibliographic record
Abstract
Several applications of body scanning technology have been commercialized or are currently under development. The virtual fit from 3D scans is most advanced form of virtual try-on. This article is an analysis of the comparison of user preferences for domestic versus foreign 3D virtual try-on systems. For this study, domestic i-Fashion Mall (www.ifashionmall.co.kr) and a Canadian company, My Virtual Model (www.mvm.com) were selected as the most representative online retailers that offer a virtual try-on system. The respondents were comprised of 70 Korean female college students in the age group 20-29. A five point Likert scale was used to evaluate the degree of the preference of virtual avatar and try-on images. T-test, cross table, and a chi-square independence test were conducted for data analysis. The results are as follow. 1. The representation about current looks according to each virtual fit image indicates that MVM is more accurate than i-Fashion Mall. 2. About decision confidence, respondents have decision confidence in i-Fashion Mall in the case of the avatar image; however, respondents have confidence in MVM or the fit image. 3. There were no significant differences in among waist size groups in accuracy, trust of each avatar image, while there were significant differences among waist size groups in the accuracy and trust of each virtual fit image. 4. About ease of use, respondents answered that i-Fashion Mall is superior to MVM. 5. The respondents prioritized the ‘fitting report’ of i-Fashion Mall and ‘Weight loss’ of MVM over other functionalities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".