Inpainting

Inpainting is an image editing technique to reconstruct or restore missing or damaged portions of an image. AI inpainting refers to using machine learning models to automatically fill in gaps:

• Models like generative adversarial networks can synthesize missing image areas.
• The AI generates synthetic content inferred from the surrounding image context.
• Useful for removing undesirable objects/marks from photos.
• Can hallucinate plausible image content without inventing new objects.
• AI inpainting trained on large datasets works better than classic algorithms.
• Can be used to expand/extrapolate image boundaries through outpainting.
• Prone to creating artifacts without sufficient training data variety.
• Raises copyright concerns when training uses others' photos without consent.

Inpainting leverages AI creativity for image restoration and editing. It pushes the boundaries of inferring context and plausible synthetic image synthesis. But like many generative models, it risks creating harmful, inappropriate content if not responsibly developed.

See also:

• Generative adversarial network
• Deep learning
• Computer vision
• Stable Diffusion