Tailor Me: An Editing Network for Fashion Attribute Shape Manipulation
Why this work is in the frame
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Bibliographic record
Abstract
Fashion attribute editing aims to manipulate fashion images based on a user-specified attribute, while preserving the details of the original image as intact as possible. Recent works in this domain have mainly focused on direct manipulation of the raw RGB pixels, which only allows to perform edits involving relatively small shape changes (e.g., sleeves). The goal of our Virtual Personal Tailoring Network (VPTNet) is to extend the editing capabilities to much larger shape changes of fashion items, such as cloth length. To achieve this goal, we decouple the fashion attribute editing task into two conditional stages: shape-then-appearance editing. To this aim, we propose a shape editing network that employs a semantic parsing of the fashion image as an interface for manipulation. Compared to operating on the raw RGB image, our parsing map editing enables performing more complex shape editing operations. Second, we introduce an appearance completion network that takes the previous stage results and completes the shape difference regions to produce the final RGB image. Qualitative and quantitative experiments on the DeepFashion-Synthesis dataset confirm that VPTNet outperforms state-of-the-art methods for both small and large shape attribute editing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it