Appearance-based virtual try-on learns to produce a plausible picture of a person wearing a garment. That objective does not necessarily require the system to know either the person's dimensions or the garment's size. The authors of the FIT dataset identify the resulting failure directly: current methods in their reviewed setting tend to generate well-fitted results regardless of garment or person size, partly because training data rarely contain precise measurements and deliberately ill-fitting pairs.[S13] This is not an audit of every commercial product. It is a precise warning about what visual plausibility can conceal.
7.2 An image objective can repair the very evidence a fit review needs
A fit technician looks for drag lines, gaping, restricted movement, seam displacement and insufficient or excessive volume. An image generator optimised for a clean result may instead remove wrinkles, redistribute cloth or regularise the silhouette until those signals disappear.[S13] The output can then be aesthetically preferable precisely because it has suppressed the diagnostic evidence. A reviewer who rewards only visual quality teaches the workflow to hide fit problems rather than detect them.
This failure is different from an inaccurate avatar. A dimensionally correct avatar can still receive a garment whose digital pattern has no verified finished measurements. Conversely, an accurate pattern can be placed on an avatar whose chest, shoulder, arm length or posture does not represent the intended wearer. The two errors can also cancel visually: excessive garment ease on an undersized avatar may resemble an approved block on a correctly sized one. A single polished render cannot identify which combination produced it.
GFT proposes that every virtual fit record carry the body representation identifier, its measurement revision, the garment pattern revision, the stated size, the material revision and the simulation configuration. These are proposed evidence fields, not features attributed to every try-on system. If any field is absent, the output remains an appearance study. It may guide a design conversation, but it cannot support a dimensional conclusion.
7.3 Deliberately wrong pairings reveal whether size information matters
A meaningful evaluation needs counterfactuals. The team should present the same body with the intended garment size, a smaller size and a larger size while holding pose, lighting, camera and material constant. It should then ask whether the system preserves the dimensional difference or normalises every result towards the same attractive fit.[S13] Incorrect pairings must be part of the test set, not rare accidents discovered after procurement.
The illustrative design in Exhibit 27 combines those pairings with two levels of evidence. The chest measurements are teaching assumptions, not preferred ease values or observations of any wearer. A low evidence score denotes an image-only review; a high score denotes the chapter's proposed measured sample, dimensional readback and activity review. The score is an ordinal design device, not a measured model accuracy.
The important comparison in Exhibit 27 is vertical as well as horizontal. Moving from a small garment to the matched garment changes a dimensional input. Moving from digital preview to physical evaluation changes the evidence available to the reviewer. A procurement test should report both movements separately. If two garment sizes produce visually similar outputs, the finding concerns size sensitivity, not whether the smaller garment is comfortable. If the physical small sample restricts an agreed movement, that finding belongs to the physical fit record, not to an image-quality leaderboard.
7.4 Procurement questions should expose the evaluation boundary
Before adopting a system, the maker should request its input schema and evaluation cases. Does it consume numerical body and garment measurements, or only photographs? Can a reviewer supply an incorrect size deliberately? Does the evaluation set include such mismatches, and are outcomes reported by pairing rather than only in aggregate?[S13] Can the team export the garment measurements used for a result and reconcile them with the pattern revision?
These questions follow the contextual evaluation logic of the NIST AI Risk Management Framework: test conditions should represent intended use, and the organisation must set its own tolerance for residual risk.[S19] A vendor demonstration with one attractive, correctly matched image answers none of them. The buyer needs retained cases, declared denominators and the freedom to record a failure.