Virtual Try-On
Unlock unprecedented capabilities in Virtual Try-On through AI Computer Vision.
AI Computer Vision Capabilities In Virtual Try-On

Accurate Product Placement
Computer vision accurately places virtual products onto the user’s image in real-time, ensuring a seamless try-on experience.

Realistic Rendering
Advanced computer vision algorithms render virtual products realistically, taking into account factors like lighting, shadows, and reflections.

Facial Recognition
Computer vision identifies facial features and contours to ensure proper alignment and fit of virtual products on the user’s face or body.

Gesture Detection
Computer vision detects user gestures for intuitive interaction, such as rotating, zooming, or removing virtual products during the try-on process.
The Value Of Applying AI Computer Vision To Virtual Try-On
Enhanced Customer Experience
Customers can visualise how products will look on them before purchasing, reducing uncertainty and increasing confidence in their buying decisions.
Reduced Return Rates
By providing a more accurate representation of product fit and appearance, virtual try-on applications help reduce return rates, saving retailers money on return logistics and restocking.
Increased Conversion Rates
The immersive and interactive nature of Virtual Try-On experiences can lead to higher conversion rates as customers are more likely to make a purchase after trying on products virtually.
Cost Savings
Virtual try-on applications eliminate the need for physical samples or in-store try-on facilities, reducing costs associated with inventory management and retail space.
Tuba.AI: Your Gateway To Build AI Vision Models Effortlessly
Tuba.AIÂ stands as your one-stop platform for every stage of AI computer vision model development. From seamlessly labelling data images to efficient training processes and straightforward deployment, Tuba.AIÂ empowers you to build robust models for virtual try-on applications with ease.
For example, Tuba.AIÂ allows developers to train AI computer vision models using labelled image datasets of clothing or accessories, allowing for accurate virtual product placement and realistic rendering during the try-on experience.