Context-Aware Mask Generation for Fine-Grained Object Insertion
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Solution Overview
Problem
Conventional image generation models face challenges in accurately inserting objects into images using coarse bounding boxes or user-scribble masks, leading to background artifacts and limited control over fine-grained scene elements, particularly in preserving the background scene and generating diverse object placements.
Innovation Solution
A method and system that utilizes a segmentation model to generate a layout mask from an input image, combined with a mask generation model trained on an input prompt and layout mask to create a fine-grained image mask, allowing precise object placement and shape definition, while preserving the background scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coarse bounding boxes or user-scribble masks are used for object insertion, then the object placement control is simplified, but background artifacts increase and fine-grained scene element control is lost
Solution Approach 1:
The patent segments the image into multiple semantic regions using a segmentation model to generate a layout mask that identifies different scene elements (e.g., sky, ground, objects). This segmentation enables precise control over where objects should be inserted while preserving background regions, resolving the contradiction between ease of operation and background preservation accuracy.
Solution Approach 2:
The patent introduces a layout mask as an intermediary between the user's simple object placement intent and the complex image generation process. The layout mask serves as a bridge that translates coarse user input into fine-grained control signals for the image generation model, enabling both ease of operation and high precision background preservation.
2Device complexity
If coarse bounding boxes are used for object insertion, then the operation process is simplified, but control over fine-grained scene elements is limited
Solution Approach 1:
The segmentation model divides the image into distinct semantic regions (sky, ground, objects, etc.) and generates a layout mask that provides fine-grained control over scene elements. This segmentation approach maintains operational simplicity while dramatically improving scene element control precision, as the layout mask automatically identifies precise boundaries without requiring complex user input.
Solution Approach 2:
The system performs automatic scene analysis and layout mask generation without requiring complex user operations. The segmentation model and layout mask generation process serve themselves by automatically identifying scene elements and their boundaries, eliminating the need for users to manually define fine-grained control parameters while maintaining high precision.
3Manufacturing precision
If layout mask and mask generation model are used, then object placement precision and background preservation are improved, but the system complexity increases
Solution Approach 1:
The layout mask serves multiple functions simultaneously: it guides object placement, preserves background regions, and provides semantic scene understanding. This multi-functionality reduces the need for separate specialized components, thereby improving object placement precision and background preservation while limiting the increase in system complexity.
Solution Approach 2:
The segmentation model and layout mask generation are performed as preliminary actions before the main image generation process. By pre-computing the layout mask that encodes scene structure and object placement guidelines, the system achieves high object placement precision and background preservation while keeping the main generation process relatively simple, as the heavy lifting of scene analysis is already done.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining an input prompt and a layout mask, where the input prompt describes a first element and the layout mask includes a second element. A mask generation model generates an image mask based on the input prompt and the layout mask, wherein the image mask includes a first region corresponding to the first element and a second region corresponding to the second element. The mask is provided to an image generation model for generating a synthetic image, where the synthetic image depicts the first element in the first region and the second element in the second region.