Content-Conditioned Style Encoding for Structure-Preserving Transfer
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Solution Overview
Problem
Current unsupervised image-to-image translation methods fail to preserve the structure of objects in content images when applying styles from untrained neural networks.
Innovation Solution
A novel approach using a content-conditioned style encoder (CCSE) and an image decoder to apply styles from untrained neural networks to content images, enabling the translation of images into latent spaces and back to generate styled output images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current unsupervised image-to-image translation methods are used, then style transfer can be achieved, but structure of objects in content images is not preserved
Solution Approach 1:
The patent segments the style encoding process into two distinct components: a style encoder that extracts style features from the style image, and a content encoder that extracts content features from the content image. This segmentation allows independent optimization of style transfer capability while preserving content structure through separate feature extraction pathways that are later combined in the decoder.
Solution Approach 2:
The patent introduces a style code as an intermediary representation that mediates between the style image and the content image. The style encoder transforms the style image into a compact style code, which then serves as a conditional input to the decoder along with content features. This intermediary approach enables style transfer without directly manipulating the content structure, thereby resolving the contradiction between style adaptability and structure preservation.
2Use of energy by moving object
If neural networks are not trained on a style by a training framework, then computational resources are saved, but the network cannot apply the style effectively
Solution Approach 1:
The patent copies style information from a style image into a compact style code representation through the style encoder. This copy approach allows the network to apply styles without requiring extensive training on each style, as the style features are directly extracted and copied from reference images. This eliminates the need for computationally expensive training while maintaining effective style application capability.
Solution Approach 2:
The patent changes the parameter representation of style from trained network weights to extracted style codes. Instead of training network parameters to learn style representations, the system extracts style parameters directly from style images using the style encoder. This parameter change approach enables style transfer without training while preserving the ability to apply diverse styles by simply changing the input style code.
Data Source
AI summary
Apparatuses, systems, and techniques to facilitate application of a style, for which one or more neural networks have not been trained by a training framework, from one image to content of another image. In at least one embodiment, a styled output image is generated by one or more neural networks based on a style contained in a style image and content of a content image where said one or more neural networks have not been trained by a training framework on said style.