Commercial systems represent cloth as a mesh of particles joined by elements that resist stretch, shear and bending, and they solve for the resting shape step by step. The implicit integration scheme of Baraff and Witkin (1998) made such simulation stable enough for practical use. Three inputs govern the outcome. The first is the avatar, which may be a parametric body adjusted by measurements or a mesh taken from a body scan. The second is the fabric, entered as tensile, shear and bending parameters and weight; these descend from the objective measurement tradition of Kawabata (1980), though each vendor uses its own test kit and units (Power, 2013). The third is the simulation setting itself: mesh density, collision thickness and friction.

Song and Ashdown (2015) compared virtual and real fit of trousers and found that the simulation was more accurate in some garment areas than in others, with overall fit judgements differing between the two. Lim and Istook (2011) showed that the drape of a simulated garment changes visibly with the fabric parameters supplied, and Power (2013) reported that converting laboratory fabric measurements into the values a simulator expects is far from straightforward. A review of sizing and fitting research concluded that validation has lagged behind adoption (Gill, 2015). None of these studies was designed around plus-size bodies.

Several features of larger bodies strain the assumptions built into simulation. Most avatars are rigid: the garment deforms and the body does not. On a real body a waistband, bra band or fitted sleeve compresses soft tissue and displaces it, changing the silhouette above and below. Shape varies widely among women with the same girths, in the position of abdominal fullness, the prominence of the seat and the fullness of the upper arm (Alexander et al., 2012). Parametric avatars enlarged from a standard form tend to distribute volume evenly and miss that variety. Body surfaces also touch one another at the inner thigh, under the bust and at the underarm, where scans have gaps and collisions are difficult to resolve. Finally, garments for this market rely heavily on stretch fabrics, whose behaviour under sustained extension is among the harder properties to parameterise.

The test set should span the cases that matter commercially: at least one woven and one knitted fabric, and garments for the upper and lower body, for example a fitted woven shirt, a woven trouser and a jersey dress. Fit is assessed at named locations, such as bust, upper arm, waist, high hip, seat, thigh and crotch, and against the classical criteria of ease, line, grain, balance and set (Fan et al., 2004). A short ordinal scale is used at each location, running from much too tight through acceptable to much too loose, together with a yes or no decision on whether the garment would be approved. Scale anchors are written out with photographs before assessment begins.

Women are invited so that the group covers the size range of interest and, within each size, differing body shapes. Each is measured manually using landmark definitions from the international standard (ISO, 2017) and scanned in a standard posture in close-fitting undergarments, with scan-derived dimensions checked against the manual ones in the manner of the scanning methodology standard (ISO, 2018). The foundation garments worn for scanning are the ones worn for fitting, since a change of bra alters the bust point and contour. Participation requires informed consent and ethical review, and scans are stored as sensitive personal data.

An avatar is prepared from each scan by closing holes and smoothing only as far as the software requires. Its key girths and lengths are checked against the manual measurements, and discrepancies beyond a stated tolerance are corrected or recorded. Where a company normally works with parametric avatars, a second avatar is produced by entering the woman's measurements into the software, so that the protocol can show how much accuracy depends on having the real body shape.

Each fabric is tested with the kit or procedure that the software vendor specifies, on conditioned specimens, and the resulting parameters are entered without adjustment. Tuning fabric values by eye until the simulation looks right is common in practice and must be excluded here, because it would use knowledge of the physical garment to improve the virtual one.

The graded production pattern for the woman's size is imported and stitched. Software version, mesh density, collision settings and the number of simulation steps are fixed in a written specification before the first run and held constant. The operator works without sight of the sewn garment on the body. Outputs are still images from set viewpoints, a turntable animation, strain and pressure maps, and the virtual ease at each fit location, taken as garment girth minus avatar girth.

The same pattern is cut and sewn in the same fabric. The model wears the sample standing, seated, and with arms raised and reaching forward. A panel of at least three experienced fit technicians rates every location independently on the prepared form. Ease is measured as the difference between garment and body girth, and where a clothed scan can be taken it is kept as a record for visual fit analysis of the kind described by Ashdown et al. (2004). The wearer's own comfort ratings are collected separately, since satisfaction with fit is partly a matter of how a person feels about her body (LaBat and DeLong, 1990) and should not be merged with the technicians' judgement.

The same panel rates the virtual garments from the simulation outputs. Order is randomised, a suitable interval separates the two sessions, and raters are not told which avatar corresponds to which woman.

Agreement, not correlation, is the quantity of interest: two methods can rise and fall together while one is consistently tighter than the other. For the ordinal ratings the protocol uses weighted kappa, which gives partial credit for near misses (Cohen, 1968), read against the conventional benchmarks of Landis and Koch (1977) and reported separately for each location and garment. For measured ease it uses the limits-of-agreement method of Bland and Altman (1986), which shows both the average bias of the virtual value and the range within which most differences fall. Consistency among raters within each mode is expressed as an intraclass correlation coefficient, chosen and reported as Koo and Li (2016) recommend. The approve or reject decisions are cross-tabulated. Before any data are seen, the team states the level of agreement that would justify relying on virtual approval, and the size of ease difference that a technician would regard as a different fit.