How AR try-on works
AR-based try-on requires the shopper to point their camera at themselves in real time. The system tracks body pose using a camera feed, then overlays a 3D model of the garment onto the video stream. This demands a 3D asset for every SKU — typically created through photogrammetry or manual 3D modeling — which costs $50–$500 per item depending on complexity.
Delivery is either through a native app or via WebXR in a browser, which as of 2026 has uneven support across mobile devices. AR works best on accessories that sit on a fixed surface, such as glasses on a nose bridge or rings on a finger, because rigid body parts are easier to track than fabric that drapes and moves with the body.
How AI photo-based try-on works
AI photo-based try-on asks the shopper to upload a single photo. The system uses a generative model — in Photta's case, Nano Banana 2, fine-tuned for apparel — to render the selected garment realistically onto the shopper's photo. No live camera session is needed, and no per-SKU 3D asset is required: the AI reads the 2D product photo directly.
Processing typically takes 8–15 seconds and delivers a photorealistic result the shopper can inspect at full resolution. The workflow is browser-native and runs inside a lightweight iframe widget, making installation a single script tag. Because the approach is render-on-demand, it scales to catalogs of any size without per-product setup cost.
Conversion data: what each approach delivers
Published studies on AR try-on generally report 20–30% reductions in product returns for accessories categories (glasses, jewelry) where AR tracking is most accurate. Conversion lift figures for AR on apparel are less consistent, partly because apparel AR rendering quality degrades when fabric movement is involved.
Photta cohort data on AI photo-based try-on shows 18–28% conversion lift on product pages with the widget active, and 25–30% return-rate reduction within 90 days. These figures hold across apparel, jewelry, and swimwear. The primary driver is shopper confidence: seeing themselves in the item resolves fit uncertainty without requiring them to be in a well-lit room with a front-facing camera.
Installation and operational complexity
AR try-on implementation typically involves a native SDK integration or a specialized WebXR partner. Each new SKU requires a 3D asset to be created, reviewed, and uploaded. For a catalog of 500 SKUs, that means 500 discrete production jobs before a single shopper can try anything on. Ongoing maintenance includes updating 3D assets when product photography changes.
AI photo-based try-on installs via a single script tag and reads your existing 2D product images. Photta's widget goes live in under 30 seconds on Shopify, WooCommerce, BigCommerce, Magento, or any custom storefront. There is no per-SKU production queue. Adding a new product to the catalog requires no additional action: the AI processes the product photo at try-on time.
When to choose AR, when to choose AI photo-based
AR has a genuine advantage in two scenarios: rigid accessories where precise placement matters (glasses fitting, ring sizing) and beauty applications (lip color, foundation shade). In these cases, real-time overlay on a live camera feed is meaningfully more useful than a static render. If your catalog is exclusively eyewear or cosmetics, AR is worth evaluating.
For everything else — apparel, jewelry on draped necklines, swimwear, outerwear — AI photo-based try-on is the better practical choice. It removes the 3D asset production bottleneck, works on any device with a browser, and delivers comparable or superior conversion outcomes at a fraction of the per-SKU cost. The right answer is the one that is actually deployable at your catalog's scale.