Context-aware object inpainting using machine learning models

Abstract

Approaches are described for automatically determining the optimal location to inpaint objects into an image to eliminate the need for manual mask creation. Segmentation models are used to analyze and classify regions within an input image and identify suitable locations for object placement based on user prompts. Multiple candidate objects may be generated using a text-to-image model, and these objects are adjusted to fit the related regions based on user prompt. The adjustment may include adjusting object parameters including size, aspect ratio, and other contextual factors for natural integration into the scene. Post-processing steps, such as adjustments based on the image's color profile, are further applied to enhance the visual consistency and quality of the final result. Multiple final images may be generated, each presenting variations in object placement and positioning, from which the user can select one or more preferred options.

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