GENERATING A MODIFIED DIGITAL IMAGE UTILIZING A HUMAN INPAINTING MODEL

Smart overview of the Invention

The patent application discusses a system for modifying digital images using advanced machine learning models. The system is designed to perform scene-based editing by understanding images as real-world scenes, allowing for intelligent and intuitive modifications. This approach leverages artificial intelligence to facilitate tasks like human inpainting, reposing, and facial expression animations, reducing the need for extensive user input.

Background

Recent advancements in computer vision have enabled sophisticated image editing capabilities, such as object identification and style transfer. The described system builds upon these technologies by employing AI models to understand and edit digital images more effectively. This method allows for seamless integration of real-world conditions into two-dimensional images, enhancing the editing experience.

System Functionality

The system utilizes machine learning to anticipate and prepare for potential edits by analyzing and understanding the digital image. It identifies distinct semantic areas within the image, treating them as components for editing rather than individual pixels. This pre-processing step allows for efficient and consistent modifications that align with real-world characteristics without requiring additional user input.

Image Pre-processing

The system pre-processes images to facilitate object-aware modifications, such as moving or deleting objects. It employs segmentation neural networks to create object masks and uses hole-filling models for content fills. These pre-generated elements enable quick and accurate modifications once user input is received, streamlining the editing process.

Semantic Scene Graphs

The system generates semantic scene graphs to map out image characteristics, including object attributes and relationships. These graphs support relationship-aware modifications by identifying connected objects and facilitating comprehensive edits. User interactions with these attributes allow for dynamic changes, enhancing the overall flexibility of digital image editing.