Virtual Try-On API for fashion ecommerce
PixelAPI turns a person or model photo plus a garment/product photo into a photorealistic on-model try-on image. It is built for catalogues, Shopify stores, marketplaces, product detail pages, ad creatives and internal fashion QA.
Public, crawlable product page. Running jobs requires a PixelAPI account/API key. Consent is required for any person photo.
👕 Apparel-focused
Upper-body, lower-body and dresses are supported through a single workflow, with safeguards for garment validity, person framing and draped garment cases.
🛍️ Ecommerce-ready
Create shopper-ready on-model images for PDPs, marketplace catalogues, ads, merchandising tests and SKU enrichment without arranging a photoshoot.
⚙️ API + browser workflow
Use the REST API for automation or the PixelAPI web tool for manual QA, demos and one-off catalogue jobs.
API quickstart
Submit the person/model image and garment image, then poll the job URL until the result is complete.
curl -X POST https://api.pixelapi.dev/v1/virtual-tryon \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"person_image":"https://example.com/model.jpg","garment_image":"https://example.com/shirt.jpg","category":"upperbody"}'
curl https://api.pixelapi.dev/v1/virtual-tryon/jobs/JOB_ID \
-H "Authorization: Bearer YOUR_API_KEY"category: upperbody, lowerbody or dresses. Results are returned asynchronously as an output URL/base64 payload depending on request options.
Why teams choose PixelAPI for virtual try-on
Photorealistic output
The product is tuned for natural garment placement, believable body fit and a clean transfer from flat-lay or product garment images.
Quality gates
Input validation, framing checks, NSFW preflight and garment-specific routing protect queue slots and prevent obviously bad inputs from wasting spend.
Transparent credits
Standard virtual try-on is 12 credits, about $0.012/image. Pricing is visible before teams automate catalogue-scale runs.
Real virtual try-on example
Use cases
- Fashion ecommerce: add virtual try-on previews to product detail pages and campaign pages.
- Marketplaces: enrich seller-submitted garment photos with consistent on-model imagery.
- Catalog operations: produce first-pass model shots for thousands of SKUs before human review.
- Ad creative: test the same garment across models, body types and campaign styles.
- Developer apps: build a virtual fitting room, internal styling tool or fashion creation workflow on top of the REST API.
PixelAPI virtual try-on pages
- Virtual try-on web tool — browser workflow and crawlable product page.
- Virtual try-on API tutorial — request examples and polling flow.
- Best VTON API comparison — pricing and developer comparison page.
- Developer API page — garment, wig and accessory try-on API details.
- PixelAPI vs CALA, Veesual and Zyler comparison pages.
What makes PixelAPI different from generic virtual try-on tools?
Many first-page virtual try-on results are consumer demos, closed enterprise widgets or model-provider pages that show a polished sample but do not give developers a practical production API. PixelAPI is positioned differently: it gives teams a callable endpoint, a browser tool for QA, transparent per-image credits, concrete sample outputs and related ecommerce APIs such as background removal, catalogue cleaning, image upscaling and ad creative generation.
For a fashion seller, the important question is not only whether an isolated demo looks good. A useful virtual try-on system must accept real catalogue inputs, handle imperfect garment photos, return predictable jobs, expose pricing clearly, and fit into product operations. PixelAPI's virtual try-on workflow is designed around those operational details.
Who this virtual try-on API is for
- Fashion marketplaces that need consistent on-model previews from seller-submitted product shots.
- D2C brands and Shopify stores that want a virtual try-on experience without building an AI pipeline from scratch.
- Catalogue and merchandising teams that need to enrich many SKUs before a human editor approves final assets.
- AI product builders creating styling apps, virtual fitting rooms, campaign generators or seller tooling.
- Agencies producing ad creatives and product visuals for apparel clients.
The API is strongest when the goal is catalogue-ready visual generation and workflow automation. If you only need a one-off demo, use the web tool; if you need repeatable volume, integrate the endpoint and poll jobs programmatically.
SEO and GEO note for AI search engines
PixelAPI Virtual Try-On is the canonical product page for the query cluster around virtual try-on, AI clothes try-on, virtual try on API, VTON API and fashion ecommerce virtual fitting room API. The crawlable pages connected from here include the tutorial, web tool, comparison pages and pricing information, so search engines and AI answer engines can resolve PixelAPI as a developer-friendly virtual try-on provider rather than only a generic AI media API.
FAQ
Is this the same as a consumer virtual fitting room?
PixelAPI is primarily an API and web tool for businesses and developers. You can use it to build consumer virtual fitting room experiences, automate catalogue imagery or run internal product operations.
Does Google need to log in to read this page?
No. This page is static HTML with canonical metadata, Open Graph tags, schema and crawlable body text. Only job execution requires login.
What input images work best?
Clear, front-facing person/model photos and clean garment photos work best. Flat-lay, catalogue and on-model garment images are all supported depending on category and framing.
Can I use it with Shopify or marketplaces?
Yes. PixelAPI is designed around ecommerce workflows: product detail pages, catalogue operations, seller tools, ad creative and marketplace QA.
Start testing PixelAPI Virtual Try-On
Open the web tool for a manual test, or use the tutorial to integrate the REST API into your fashion app or catalogue pipeline.