Two-Stage Conditional Image Generation Architecture
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Solution Overview
Problem
Current image generation systems lack the ability to produce diverse and high-fidelity images, and are not controllable in terms of factors such as posture, style, background, and fine-grained details.
Innovation Solution
The system employs a two-stage generator architecture, where the first stage generates an intermediate representation of an image from a random noise sample without specific conditions, and the second stage uses this representation to create a conditional image based on user-specified styles, allowing for controlled generation of images with consistent structure and varying styles or details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single-stage generator is used for image generation, then the system complexity is low, but the image diversity and fidelity are insufficient
Solution Approach 1:
The image generation system is divided into two distinct stages: a structure prior generator that captures high-level structural information from random noise, and a conditional generator that refines the image based on user-specified conditions. This segmentation allows each stage to specialize in specific aspects of image generation, improving overall fidelity and diversity while maintaining manageable system complexity through modular architecture.
2Manufacturing precision
If conditional image generation is implemented, then image fidelity to specific conditions is improved, but controllability over factors like posture and style is lost
Solution Approach 1:
The structure prior generator performs preliminary action by generating an intermediate representation that captures high-level structural information (posture, shape, layout) before the conditional generation stage. This preliminary structuring allows the conditional generator to focus on refining style and appearance details while preserving structural controllability, enabling users to control both structure and style independently.
3Adaptability or versatility
If random sampling is used for image generation, then image diversity is achieved, but image fidelity to specific conditions is poor
Solution Approach 1:
An intermediate representation serving as a mediator is generated by the structure prior generator, containing high-level structural information. This intermediate representation acts as a bridge between random sampling (for diversity) and conditional generation (for fidelity). The conditional generator uses this intermediate structure as input and refines it according to specific conditions, thereby achieving both diversity from the random noise input and fidelity through condition-based refinement.
Data Source
AI summary
A first generator stage of an image synthesis computer system generates an intermediate representation of an image that captures high-level structure of the image. The intermediate representation is generated from a random noise sample and without specific condition provided for the image. A second generator stage of the image synthesis computer system generates a conditional image based on i) the intermediate representation of the image and ii) a condition provided for the image, the condition specifying a desired style for the image. As a result, the conditional image is generated to match i) the high-level structure captured in the intermediate representation of the image and ii) the desired style specified by the condition provided for the image.