SellerPic

SellerPic is a background tool that turns one white-background product shot into a full set of store-ready visuals. SellerPic is an AI production platform for online retailers that turns a single white-background product photo into model shots, lifestyle scenes, virtual try-on images, retouched catalogue visuals and short shoppable videos.

SellerPic is a production studio condensed into a web app for e-commerce sellers. The starting point is deliberately modest: a single white-background product photo. From that asset the platform generates fashion model shots, lifestyle scenes, product-in-hand compositions, spotlight images and short shoppable videos. The stated pitch is one input image feeding creative for the whole funnel, from catalogue stills to social ad clips. The toolkit is broad and clearly organised around retail tasks rather than generic image generation. Virtual try-on covers jewelry in detail, including rings, earrings, necklaces, bracelets and watches, alongside apparel and accessories. Utility tools handle background removal and replacement, color changes, erasing, retouching, upscaling, image extending and instruction-based editing. A separate AI agent layer covers ad cloning and social ad generation, and the site lists chat assistants built on ChatGPT, Gemini, Claude, Grok and Perplexity for merchants who want model choice inside one workspace. In practice, a seller uploads a product cutout, picks a tool or scene type, and receives finished assets in marketplaces or formats suited to Amazon, Shopify, Meta and similar channels. The homepage positions the output as e-commerce ready, with delivery described in hours rather than the weeks a studio shoot takes. Shoppers and store owners on Shopify get a native app path, while other merchants work through the web app. Where does it sit among alternatives? Single-purpose background removers and try-on tools do one job cheaply. SellerPic competes on breadth: image, video, ad copy angles and publishing in one place, with unlimited royalty-free usage on outputs. The trade-off is that breadth invites comparison against specialist tools in each category, and buyers should test output fidelity on their own catalogue before scaling.