Image Generation Neural Network Geometric Consistency
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Solution Overview
Problem
Existing image generation methods struggle to maintain three-dimensional geometry consistency when transforming images, leading to unrealistic outputs with noticeable straight lines and perspective issues, making it difficult to distinguish between real and fake images.
Innovation Solution
The method involves an image generation neural network that receives conditional information and depth information, extracts feature information, and transforms images to maintain geometric consistency by training on a comparison between synthesized images based on transformation relationships, using adversarial and geometry consistency losses to preserve structural information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing transformation methods are used to generate images with restricted structure (indoor space or cityscape), then image generation is possible, but three-dimensional geometry cannot be fully considered, resulting in straight lines and perspective mismatches
Solution Approach 1:
The patent introduces depth information as an intermediary element that bridges the input image and the generated image. This depth information acts as a mediator to convey three-dimensional geometric relationships, enabling the transformation to preserve perspective and straight lines while maintaining the ability to generate images with restricted structures.
Solution Approach 2:
The patent adds a new dimension to the image transformation process by incorporating depth information. This dimensional addition allows the system to consider three-dimensional geometry during transformation, resolving the contradiction between ease of image generation and geometric precision by operating in an extended feature space that includes depth.
2Manufacturing precision
If conditional information is transformed based on depth information, then geometric consistency is improved, but the complexity of the transformation process increases
Solution Approach 1:
The patent segments the transformation process into distinct components: extracting depth information, transforming conditional information based on depth, and generating the final image. This segmentation allows each component to be optimized independently, managing overall complexity while achieving geometric consistency through the coordinated action of these modular components.
Data Source
AI summary
A method and apparatus for generating an image and for training an artificial neural network to generate an image are provided. The method of generating an image, including receiving input data comprising conditional information and image information, generating a synthesized image by applying the input data to an image generation neural network configured to maintain geometric information of the image information and to transform the remaining image information based on the conditional information, and outputting the synthesized image.