Virtual Try-On FAQ - Free AI Fitting Room Questions

Virtual try-on uses advanced AI and computer vision technology to analyze your photo and clothing images. The AI detects your body shape, pose, and the clothing item, then generates a realistic composite showing how the clothing would look on you. The process involves face detection, body segmentation, and advanced image processing to create natural-looking results.

For best results, you need a clear front-facing photo of yourself with good lighting. The photo should show your full upper body (from head to waist or below). Avoid photos with busy backgrounds, sunglasses, or face coverings. For clothing, use clear product photos on a plain background. The better the quality of your photos, the more accurate the virtual try-on will be.

Our virtual try-on technology uses state-of-the-art AI models trained on thousands of images to provide highly accurate results. While it's not 100% perfect (as it's a simulation), it gives you a very good idea of how clothing will look and fit. The accuracy depends on photo quality, lighting, and the type of clothing. We continuously improve our AI models to provide better results.

You can try on dresses, shirts, t-shirts, jackets, suits, sweaters, pants, and clear shopping screenshots. The technology works best when the clothing item is visible, not heavily blocked, and easy to separate from the background.

Typically, virtual try-on takes 30-60 seconds to process. The time depends on server load and image complexity. Pro and Pro+ members get priority processing, so their requests are usually faster.

Free users start with 3 credits, enough for one standard fitting-room preview. Pro members get 20 credits per month, and Pro+ members get 80 credits per month.

Yes, absolutely. Your photos are processed securely and are never shared with third parties. We use enterprise-grade encryption and follow strict privacy policies. You can delete your photos and data at any time from your account settings.

Currently, you can try on one clothing item at a time. However, you can generate multiple try-on results and compare them. We're working on features to support trying on multiple items simultaneously in the future.