What is inpainting?
Inpainting is an AI technique that regenerates a selected part of an image while leaving everything outside that selection unchanged. You mark a region with a mask, and the model fills it with new content that fits the surrounding pixels in lighting, perspective, and texture. It's used to remove an unwanted object, replace a background, repair a defect, or swap one element for another — all without redrawing the whole picture.
The defining property is locality. A normal text-to-image generation builds an entire frame from scratch; inpainting changes one area and is constrained to blend seamlessly with the rest. That constraint is what makes it an editing tool rather than a generation tool, and it's why inpainting is central to any workflow that needs to alter a real photo precisely instead of producing a new one.
How inpainting works
Inpainting starts with a mask: an extra channel where white marks the pixels to regenerate and black marks the pixels to keep. A diffusion model then denoises the image step by step, but at every step it re-inserts the known, unmasked region from the original so the model only has freedom inside the mask. Because the model can see the surrounding context at each step, the generated area inherits the same color, shadow direction, and grain as the kept pixels, which is what makes a good edit invisible.
Inpainting models often add input channels to the network specifically for the masked image and the mask, so the model is trained to treat "fill this region given everything around it" as its actual task rather than approximating it.
Common uses
- Removing distractions: stray objects, blemishes, logos, or background clutter
- Replacing backgrounds while keeping the subject intact
- Restoring damaged or incomplete photos
- Swapping or adding elements — changing a setting, extending a scene
Inpainting vs. outpainting and full generation
Full text-to-image generation creates a whole new image from a prompt. Outpainting extends an image beyond its original borders, inventing what would lie outside the frame. Inpainting works inside the existing borders, rebuilding a chosen region. The three are related diffusion techniques, but inpainting is the one used when most of a real photograph must be preserved exactly and only a defined area should change.
Why inpainting matters for fashion ecommerce
Fashion imagery is full of targeted edits: clean up a wrinkle, remove a clip holding a garment in place, change a background to match a campaign, or swap one element without reshooting. Inpainting does each of these while keeping the product itself pixel-accurate, which matters because a shopper is judging the exact item that ships. An edit that subtly alters the garment's color or pattern is worse than no edit at all.
It's also the mechanism behind a lot of AI on-model work. Generating a believable person around a real garment is, in effect, a masked-region problem: the garment is the region to preserve, and the body, hands, and scene are the regions to generate so they match its perspective and light. Strong pipelines lean on this preservation behavior; weak ones smear the seam between the real product and the generated figure.
Inpainting inside WearView
WearView holds the uploaded garment fixed while generating the model and environment around it — the same preserve-this, regenerate-that principle inpainting is built on. That's how a striped shirt keeps its stripes and a printed graphic stays legible while a photoreal model and setting appear around the product.