Virtual try-on models
Paired garment/worn imagery on consistent avatars, at volumes photography cannot reach.
Service 02 / AI
Synthetic apparel data, built by people who know garments
Most synthetic fashion data looks wrong to anyone who has made clothing. Ours is generated from real patterns and real fabric parameters, so seams, grain and drape hold up under a model's attention as well as a buyer's.
Why synthetic
- Clean rights. No scraped catalogue imagery, no model releases, no brand marks you have to defend later.
- Controlled variation. You specify the axes; every image differs only in the ways you asked for.
- Labels are free. Masks, keypoints and attributes come out of the render pipeline, not a labelling vendor.
- Long-tail coverage. Rare constructions and edge sizes that barely exist in photographed data.
Specify the set
Variation axes you control
Garment
Category, construction, silhouette, size and grade
Material
Fabric type, weight, drape, print, texture and sheen
Colour
Full colourway ranges, exact hex or Pantone targets
Avatar
Body type, skin tone, height, pose and hair
Scene
Lighting rig, background, camera height and focal length
Annotation
Segmentation masks, keypoints, attribute labels, metadata JSON
Applications
What teams train on it
Attribute tagging
Perfectly labelled construction details — collar type, sleeve length, closure, pocket style.
Generative fashion
Style-consistent training sets that keep garment structure plausible instead of melted.
Fit and size prediction
The same style rendered across a graded size run on matched body scans.
Ask for a pilot batch
Send your spec — garment types, variation axes, annotation format, target volume. We return a small sample batch with metadata so you can test it in your pipeline before committing.