Booting VTON Diffusion Core...

Pillar · Interactive Mirror

Try it on. In real time.

Q: What is the ZYNG OS Virtual Try-On (VTON) Engine?

It is a real-time Virtual Try-On engine that maps any target garment onto a single user selfie. Using custom-trained, in-house diffusion models, it aligns fabric drape, folds, shadows, and posture to the person's proportions in under 2 seconds — generating photorealistic, PDP-ready try-on imagery without a physical photoshoot.

Q: How does ZYNG VTON preserve a person's face and body proportions?

The engine locks a target identity using FaceID embedding and a 68-anchor kinematic pose grid (128 mapped keypoints). This preserves facial identity, skeletal structure, body proportions, and ambient studio lighting while the garment is synthesized onto the subject, so the output looks like the same person genuinely wearing the item.

Q: How fast is the Virtual Try-On and what does it cost to run?

Each try-on is generated in under 2 seconds at p50 latency on the Zyng-VTON-v2.1 model (FP16 precision, 20 diffusion steps, CFG 4.5). Because inference runs on proprietary in-house diffusion models rather than third-party APIs, the cost per generation is roughly 0.3x that of comparable hosted try-on services, which makes batch generation across full catalogs economical.

Q: Is Virtual Try-On (VTON) output ready for PDP deployment?

Yes. ZYNG's custom-trained VTON models utilize a 68-anchor kinematic grid and FaceID embedding to preserve facial identity, skeletal structure, ambient studio lighting, and material folds in under 2 seconds. Dozens of global brands use these outputs directly on live product detail pages, mobile applications, and social campaigns to scale lifestyle imagery.

Q: How is try-on output quality validated for fidelity?

Every generated try-on is checked against the source garment with point-by-point similarity checkpoints — logo alignment, stitch and texture seams, graphic warp contours, and collar/sleeve trims. The engine reports a Structural Match Index (SSIM ~99%), sub-pixel vector registration deviation, and chromatic drift (ΔE around 0.34) before an asset is approved for publishing.

One selfie in, an entire wardrobe out. ZYNG's custom-trained diffusion models map any garment onto a person while preserving identity, proportions, fabric drape, and studio lighting — in under 2 seconds.

<2s Latency (p50)

0.3x Inference Cost

Custom In-House Model

Open Try-On Sandbox ↓

ZYNG_VTON_v2.1 // STREAMING

Target Identity Locked

Source user selfie locked as the try-on target identity

ID_08239 · 128/128 KP

Generation Stream LOOK 01

Streetwear virtual try-on output generated from the selfie

Smart casual oxford virtual try-on output

Get Started · VTON

See real-time try-on on your own catalog.

Bring one selfie and watch ZYNG map your garments in under two seconds — identity, proportions, drape and studio lighting preserved.

Book a Demo → 15-min walkthrough · no commitment

01 Interactive Try-On Sandbox

Pipeline Live

Selfie In · Look Out

Synthesize a Look in One Click

Pick a target garment below. The engine maps it onto the same locked selfie and returns the synthesized try-on — no reshoot, no model booking.

LOOK_01 · STREET

Layered leather outerwear garment reference

LOOK_02 · OUTER

Smart casual oxford shirt garment reference

LOOK_03 · OXFORD

LOOK_04 · TAILORED

Generation Pipeline

Selfie Input

Target Garment

→

VTON Latent Output

Aligns oversized cotton hoodie drapes and shoulder seam placements to the user's proportions while preserving texture and base lighting parameters.

02 Batch Generation Stream

Streaming Outputs

One Selfie · Infinite Looks

Catalog-Scale Try-On Generation

The same locked identity fans out across your entire line. Queue a catalog and the stream returns photorealistic, channel-ready looks back-to-back — each one PDP-, app-, and campaign-ready.

BATCH_01 1.8s

BATCH_02 1.9s

BATCH_04 1.8s

BATCH_05 1.9s

BATCH_06 1.7s

BATCH_07 1.8s

Answers

Virtual Try-On, Explained

What is the ZYNG OS Virtual Try-On (VTON) Engine?

It is a tool that allows you to digitally place any clothing item onto a photo of a person. The system automatically adjusts how the fabric drapes, folds, and casts shadows so it matches the person's pose and body proportions, creating realistic images without the need for a physical photoshoot.

How does ZYNG VTON preserve a person's face and body proportions?

Our system uses body-mapping technology to recognize the model's posture, shape, and facial features. It keeps their original appearance, body proportions, and the background lighting exactly the same while naturally fitting the new garment onto them.

How fast is the Virtual Try-On and what does it cost to run?

The images are generated in just a few seconds. Because we run our own built-in models rather than relying on external third-party services, the cost per image is kept relatively low, making it practical to process entire product catalogs at scale.

Is Virtual Try-On (VTON) output ready for PDP deployment?

Yes, the outputs are designed to look natural and high-quality. Brands use these generated images directly on their main product detail pages, mobile applications, and social media campaigns to showcase their apparel on different models.

How is try-on output quality validated for fidelity?

The system compares the final try-on photo back to the original clothing item. It automatically checks key details like logo placement, seam alignment, and texture patterns to make sure the digital representation is accurate before the image is approved for your website.

Ready when you are

Ship try-on imagery at production scale.

From a single API call to PDP-ready try-on across your entire range. Let's map the pipeline to your stack.

Book a Demo → Talk to the team · contact@zyngai.com