Show the same garment on diverse body types and skin tones directly on the PDP with AI virtual try-on and on-model rendering. This inclusive e-commerce imagery improves fit confidence, reduces uncertainty, and aligns visuals to regional preferences. Powered by AI image generation, the solution delivers consistent, brand-safe outputs at scale - no studio reshoots or complex workflows. Ideal for fashion AI drops, campaign refreshes, and marketplace-ready assets.
Most catalogs rely on one model archetype, leaving shoppers to guess how a garment will drape on their body type or match their skin tone. Producing inclusive visuals through traditional photoshoots is slow, costly, and hard to replicate across regions, sizes, and channels. The result is lower PDP engagement, higher size-related returns, and visuals that miss local styling and modesty expectations - especially during rapid product drops and seasonal campaigns.
Shuru’s AI Fashion Try-On & Model Generator converts a master product image into photorealistic, on-model renders across standardized body archetypes and a balanced skin-tone palette. Computer vision preserves fabric texture, print alignment, seams, and logos; AI image generation adapts pose, proportion, and framing for PDP and marketplace standards. Regional presets control styling and modesty, while brand guardrails and evaluators ensure realism, tone consistency, and compliance.
AI Fashion Try-On & Model Generator
Convert flat-lay garment images into virtual try-ons
Higher PDP conversion: inclusive on-model previews raise confidence and add-to-cart.
Lower returns and exchanges: better fit expectation reduces size-related churn.
Faster catalog coverage: generate complete image sets without studio reshoots.
Localized engagement: region-tuned poses and styling lift CTR across markets.
Brand consistency at scale: automated guardrails keep lighting, framing, and logos on-spec.
Operational efficiency: fewer tickets and retouch loops; seamless CMS and marketplace exports.
Partner with us to design, prototype, and deploy.
