Segmentation Guided GAN for Spatially Controllable Image Generation
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Solution Overview
Problem
Existing image generation methods, particularly in image-to-image translation, produce low-quality and unrealistic images due to neglecting higher-level and instance-specific information, leading to a lack of spatial controllability during the translation process.
Innovation Solution
The implementation of a Segmentation Guided Generative Adversarial Network (SGGAN) that leverages semantic segmentation to guide the image generation process, using a segmentor network to impose spatial constraints and improve image quality by generating images that align with target segmentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image generation methods are used, then the generation process is simple, but the image quality is low and unrealistic
Solution Approach 1:
The patent introduces a segmentor network that divides the image generation process into segmented components: a generator network for creating images, a discriminator network for evaluation, and a segmentor network for generating spatial constraints. This segmentation allows each component to specialize and improve overall image quality while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The segmentor network acts as an intermediary between the generator and discriminator, providing spatial constraints and semantic guidance. This intermediary component translates high-level semantic information into spatial constraints that guide the generator, improving image realism without requiring direct complex interactions between the generator and discriminator.
2Manufacturing precision
If semantic segmentation is used to guide image generation, then spatial controllability improves, but computational requirements increase
Solution Approach 1:
The segmentor network generates spatial constraints and semantic segmentations in advance before the main image generation process. By pre-computing these guidance signals, the system reduces the computational burden during the actual generation phase, as the generator can follow pre-established spatial constraints rather than computing them in real-time.
Solution Approach 2:
The patent applies different levels of computational processing to different parts of the image generation process. The segmentor focuses computational resources on generating spatial constraints for critical regions, while the generator applies these constraints locally to specific image areas, optimizing energy usage by avoiding uniform high-computation processing across the entire image.
3Manufacturing precision
If adversarial training is implemented, then image realism improves, but training time increases
Solution Approach 1:
The adversarial training process is made continuous through the integration of the segmentor network that provides ongoing spatial constraints during training. The generator, discriminator, and segmentor are trained jointly in a continuous feedback loop, where the segmentor's spatial constraints guide the generator continuously, allowing the system to converge faster to realistic image generation while maintaining the adversarial training benefits.
Data Source
AI summary
Embodiments provide methods and systems for image generation through use of adversarial networks. An embodiment trains an image generator comprising (i) a generator implemented with a first neural network configured to generate a fake image based on a target segmentation, (ii) a discriminator implemented with a second neural network configured to distinguish a real image from a fake image and output a discrimination result as a function thereof and (iii) a segmentor implemented with a third neural network configured to generate a segmentation from the fake image. The training includes (i) operating the generator to output the fake image to the discriminator and the segmentor and (ii) iteratively operating the generator, discriminator, and segmentor during a training period, whereby the discriminator and generator train in an adversarial relationship with each other and the generator and segmentor train in a collaborative relationship with each other.