Abstract Style Transfer Presets Using Encoding and Clustering
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Solution Overview
Problem
Conventional image generation systems fail to generate satisfactory composite images when dealing with abstract backgrounds, as they are not designed to process and transfer styles from abstract images effectively, leading to user dissatisfaction and difficulty in identifying and applying style attributes.
Innovation Solution
An image processing apparatus that generates an abstract style transfer preset representing an abstract style cluster, using a style encoder to encode abstract images, clustering these encodings, and applying a multi-modal encoder to tag and name the clusters, enabling controllable preset style transfer for abstract backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image generation systems are used, then they can process standard images, but they fail to generate satisfactory composite images when dealing with abstract backgrounds
Solution Approach 1:
The patent segments the image processing task by separating style extraction from content processing. The style encoder extracts style features from abstract background images independently, creating style embeddings that can be applied to content images. This segmentation allows the system to handle abstract backgrounds effectively without compromising content quality.
Solution Approach 2:
The patent introduces style embeddings as an intermediary representation between abstract background images and content images. The style encoder transforms abstract backgrounds into compressed style embeddings, which then serve as intermediaries to transfer style attributes to content images through the image generation model, enabling reliable style transfer despite the abstract nature of source images.
2Adaptability or versatility
If abstract background images are used as style sources, then style variety can be increased, but the systems fail to identify and transfer style attributes effectively
Solution Approach 1:
The patent replaces manual style attribute identification with an automated neural network-based style encoder. Instead of requiring users to manually analyze and identify style attributes in abstract backgrounds, the style encoder automatically extracts and represents style features as embeddings, making the process efficient and accurate.
Solution Approach 2:
The patent transforms abstract background images into a different parameter space through the style encoder, converting visual style attributes into numerical style embeddings. This parameter transformation enables the system to work with abstract backgrounds by representing their style characteristics in a format suitable for computational processing and transfer.
3Ease of operation
If manual style transfer processes are used, then users have control over the process, but the process becomes complex and time-consuming
Solution Approach 1:
The patent performs preliminary action by pre-processing abstract background images through the style encoder to generate style embeddings before the actual composite image creation. This preliminary extraction of style features automates the preparatory work, reducing the time and complexity of the overall process while maintaining user control over style selection.
Solution Approach 2:
The patent enables self-service by allowing the system to automatically handle style extraction and transfer without requiring manual user intervention for each step. The style encoder automatically identifies and extracts style attributes from abstract backgrounds, and the image generation model automatically applies these styles to content images, significantly reducing processing time and operational complexity.
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure include an image generation network configured to encode a plurality of abstract images using a style encoder to obtain a plurality of abstract style encodings, wherein the style encoder is trained to represent image style separately from image content. A clustering component clusters the plurality of abstract style encodings to obtain an abstract style cluster comprising a subset of the plurality of abstract style encodings. A preset component generates an abstract style transfer preset representing the abstract style cluster.