Dot-Based Image Generation for Editable 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 input images deviate from the camera perspective.
Innovation Solution
A method and system for generating images using dots that include class information, position information, and optionally size information, allowing users to easily specify and modify objects within an image generation model, independent of its inherent camera perspective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text prompts are input as a condition for the image generation model, then the model can generate images based on textual descriptions, but users cannot easily obtain the desired images because the text prompts are abstract
Solution Approach 1:
The patent segments the abstract text prompt into structured dot data with specific spatial coordinates, class information, and size parameters. Each dot represents a specific object instance with precise positioning, transforming the abstract textual concept into concrete, manipulable spatial elements that the image generation model can process more effectively.
Solution Approach 2:
The patent introduces dot data as an intermediary representation between the user's textual intent and the image generation model. This dot data structure serves as a mediator that translates abstract text prompts into a format with explicit spatial and semantic information, making the generation process more controllable and easier to refine.
2Adaptability or versatility
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 dot data points, each representing a specific object with its position, class, and size. This segmentation allows users to modify individual objects by adjusting their corresponding dot parameters independently, rather than having to edit the entire image as a single unit.
Solution Approach 2:
The patent transforms the static image input into dynamic dot data that can be easily adjusted. Users can modify object positions, sizes, and classes by changing dot parameters, and the system can regenerate images with different configurations, providing dynamic control over the generation process.
3Measurement precision
If a bounding box is input as a condition for the image generation model, then the model can process spatial information, but image generation fails if it deviates from the camera perspective that the image generation model inherently possesses
Solution Approach 1:
The patent changes the parameter representation from simple bounding boxes to comprehensive dot data that includes position, class information, and size. This parameter expansion allows the system to encode camera perspective information explicitly within the dot data structure, enabling the model to generate images that are consistent with the specified perspective rather than failing when deviations occur.
Data Source
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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.