GarFast: Realistic and Fast Garment Transfer with a Simplified Parser-Free Approach
Bibliographic record
Abstract
A good garment try-on model should learn the transfer between different types of garments while satisfying: 1) high fidelity and 2) low inference speed. Existing methods address either of these two issues, limited processing speed or low generation quality. We directly use a lightweight encoder-decoder, ensuring faster speeds. To tackle the problem of lower image quality typically generated by lighter models, we present GarFast, a simplified, parser-free framework that optimizes the same lightweight network through a two-stage transformation of real data roles (from input to supervision), thereby greatly promoting model convergence. Specifically, first, we propose a correction strategy to prevent the difficulty of convergence caused by the lack of ground truth in the first stage. Second, we propose a fine-grained domain consistency to ensure that the results generated in the unsupervised first stage are highly realistic clothed human images. Finally, we propose a skin-variant refinement loss and a skinMix regularization to amplify texture differences and enhance the realism of skin-variant regions, thereby improving the quality of the generated skin. Extensive experiments thoroughly demonstrate that our method achieves high resolution, near real-time performance, and superior reconstruction quality compared to state-of-the-art approaches, with processing times of less than 0.03 seconds on an Nvidia A100.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".