Dot-Based Image Generation for Precise Object Placement
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Solution Overview
Problem
Existing AI-based image generation technologies face challenges in generating desired images due to abstract text prompts, difficulty in modifying input images, and failure when the camera perspective deviates from the model's inherent perspective.
Innovation Solution
A method and system that utilize dots, each containing class information, position information, and optionally size information, to generate synthesized images using an image generation model, allowing users to easily adjust object class, position, and size, and remove or modify objects within the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If text prompts are input as conditions for the image generation model, then the model can generate images based on text descriptions, but users cannot easily obtain the desired images because the text prompts are abstract
Solution Approach 1:
The patent introduces dots as an intermediary representation between abstract text prompts and concrete image generation. Dots contain structured information (class, position, size) that bridges the gap between textual descriptions and visual output, making it easier for users to obtain desired images while reducing information loss through structured data representation.
Solution Approach 2:
The patent transforms the input format from abstract text prompts to structured parameters (class information, position information, size information) represented by dots. This parameter change enables precise control over image generation elements, directly addressing the issue of abstraction and making image modification easier.
2Ease of operation
If an image is input as a condition for the image generation model, then the model can generate images based on visual input, but it is difficult for the user to modify the input image
Solution Approach 1:
The patent segments the input image into discrete objects represented by dots, where each dot corresponds to a specific object with defined properties (class, position, size). This segmentation allows users to modify individual objects independently by adjusting their corresponding dots, greatly simplifying image modification compared to manipulating the entire image or dealing with complex pixel-level edits.
3Reliability
If a bounding box is input as a condition for the image generation model, then the model can generate images with spatial constraints, but image generation fails if it deviates from the camera perspective that the image generation model inherently possesses
Solution Approach 1:
The patent extends the simple bounding box representation to include class information, position information, and size information in a unified dot structure. This parameter expansion allows the model to understand not just spatial constraints but also object identity and relative positioning, enabling successful image generation across different camera perspectives by providing comprehensive contextual information.
Data Source
AI summary
Provided is a method for generating images, which is performed by one or more processors, and includes receiving a first dot associated with a first object, and generating a first synthesized image based on the first dot using an image generation model, in which the first dot includes first class information and first position information associated with the first object, and the first synthesized image is a synthesized image in which the first object corresponding to the first class information is placed at the first position.