How AI Transfers Visual Styles Between Images While Keeping Structure
This patent describes an AI method to generate a new image that combines the structure of one input image with the visual style of another, semantically related image, using a pre-trained Vision Transformer model.
Original patent title: “Method and system for semantic appearance transfer using splicing ViT features”
This patent describes an AI method to generate a new image that combines the structure of one input image with the visual style of another, semantically related image, using a pre-trained Vision Transformer model. Granted to Yeda Research and Development Co in 2025 with 19 claims and 2 forward citations, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This method generates a new image by taking two inputs: a 'source structure image' and a 'target appearance image'. The goal is to create a 'third image' that keeps the original structure from the first image but adopts the visual look from the second. For example, if you provide a picture of a person (source structure) and a picture of a famous painting (target appearance), the system aims to redraw the person in the style of the painting. It achieves this by training a 'generator' (an Artificial Neural Network, or ANN) to minimize differences between the appearance of the generated image and the target appearance, and optionally, to minimize differences between the structure of the generated image and the source structure (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1, Claim 3). A key component is using a pre-trained, fixed Vision Transformer (ViT) model as a 'semantic prior' to understand and correctly match objects between the two input images (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →, Claim 8). This allows the generator to be trained effectively with just a single pair of input images.
The gap
What does this patent NOT cover?
- Does not cover style transfer methods that do not use a Vision Transformer (ViT) model as a semantic prior for training the generator.
- Does not cover image generation where the structure is also created or significantly altered, as it focuses on preserving the 'first structure' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover methods that require large datasets for training the generator itself, as this patent emphasizes training with only a 'single structure/appearance image pair' (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →).
- Does not cover transferring appearance without semantic awareness, meaning it aims to 'paint' semantically related objects, not just apply a general filter.
- Does not cover methods that rely solely on adversarial training, as the patent specifies training 'without adversarial training' (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The truly clever part is using a pre-trained and fixed Vision Transformer (ViT) model as an 'external semantic prior'. This allows a separate generator to be trained efficiently with only a single pair of input images, without needing extensive datasets or complex adversarial training, while still achieving high-quality, semantically accurate appearance transfer.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Virtual try-on applications for clothing or accessories
Digital fashion design tools
Content creation for advertising and media
Personalized avatar generation
Artistic style transfer applications
Special effects in film and video production
Why it matters
The bigger picture
This technology is important because it enables high-quality, semantically aware image manipulation with less training data and computational effort. By leveraging pre-trained AI models, it simplifies the process of transferring complex visual styles onto new structures. This can accelerate content creation workflows and open new possibilities for personalized digital experiences across various industries.
Filed
December 18, 2022
Granted
April 22, 2025
Market context
Who's building on this
Companies in this space
Yeda Research and Development Co Ltd, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a technology transfer company for the Weizmann Institute of Science, indicating ongoing academic and commercial research in this area. Major tech companies like Google, Meta, and Adobe, along with numerous startups in generative AI, are actively developing and refining image synthesis and style transfer technologies. These entities are continuously exploring more efficient and higher-quality methods for manipulating visual content, often building on foundational techniques like those described here.
Market impact
This patent contributes to the rapidly growing field of generative AI, particularly in image-to-image translation. By offering a method for high-quality semantic appearance transfer with reduced training data, it could lower the barrier to entry for creating sophisticated visual content. This could impact industries like digital marketing, e-commerce (e.g., virtual try-on), and entertainment by enabling more efficient and realistic content generation, potentially leading to new product features and services.
Claim 1 — Plain English
What this patent covers
This method generates a new image by taking two inputs: a 'source structure image' and a 'target appearance image'. The goal is to create a 'third image' that keeps the original structure from the first image but adopts the visual look from the second. For example, if you provide a picture of a person (source structure) and a picture of a famous painting (target appearance), the system aims to redraw the person in the style of the painting. It achieves this by training a 'generator' (an Artificial Neural Network, or ANN) to minimize differences between the appearance of the generated image and the target appearance, and optionally, to minimize differences between the structure of the generated image and the source structure (Claim 1, Claim 3). A key component is using a pre-trained, fixed Vision Transformer (ViT) model as a 'semantic prior' to understand and correctly match objects between the two input images (Abstract, Claim 8). This allows the generator to be trained effectively with just a single pair of input images.
The clever bit
The truly clever part is using a pre-trained and fixed Vision Transformer (ViT) model as an 'external semantic prior'. This allows a separate generator to be trained efficiently with only a single pair of input images, without needing extensive datasets or complex adversarial training, while still achieving high-quality, semantically accurate appearance transfer.
What it does not cover
- Does not cover style transfer methods that do not use a Vision Transformer (ViT) model as a semantic prior for training the generator.
- Does not cover image generation where the structure is also created or significantly altered, as it focuses on preserving the 'first structure' (Claim 1).
- Does not cover methods that require large datasets for training the generator itself, as this patent emphasizes training with only a 'single structure/appearance image pair' (Abstract).
- Does not cover transferring appearance without semantic awareness, meaning it aims to 'paint' semantically related objects, not just apply a general filter.
- Does not cover methods that rely solely on adversarial training, as the patent specifies training 'without adversarial training' (Abstract).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
10/40
Early citations
Claim breadth
13/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$94K – $300K
Midpoint $187K · 16.3 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
19 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
TUMANYAN, N., Dekel, T., Bagon, S., & TAL, O. B. (2025). How AI Transfers Visual Styles Between Images While Keeping Structure (U.S. Patent No. 12,282,696). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12282696/method-and-system-for-semantic-appearance-transfer-using-splicing-vit-features
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How AI Transfers Visual Styles Between Images While Keeping Structure cover?
This patent describes an AI method to generate a new image that combines the structure of one input image with the visual style of another, semantically related image, using a pre-trained Vision Transformer model.
Who owns patent US 12282696?
Yeda Research and Development Co owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on December 18, 2042, when the invention enters the public domain.
What is patent US 12282696 cited by?
This patent has been cited by 2 later patents that build on its ideas.
What problem does this patent solve?
This technology is important because it enables high-quality, semantically aware image manipulation with less training data and computational effort. By leveraging pre-trained AI models, it simplifies the process of transferring complex visual styles onto new structures. This can accelerate content creation workflows and open new possibilities for personalized digital experiences across various industries.
What does this patent NOT cover?
Does not cover style transfer methods that do not use a Vision Transformer (ViT) model as a semantic prior for training the generator.
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