Google llc (20240104312). Photorealistic Text Inpainting for Augmented Reality Using Generative Models simplified abstract
Contents
- 1 Photorealistic Text Inpainting for Augmented Reality Using Generative Models
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Photorealistic Text Inpainting for Augmented Reality Using Generative Models - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
Photorealistic Text Inpainting for Augmented Reality Using Generative Models
Organization Name
Inventor(s)
Thomas Jonathan Stone of Zurich (CH)
Darkhan Zholmukhanov of Adliswil (CH)
Dawid Michal Wegner of Zurich (CH)
Photorealistic Text Inpainting for Augmented Reality Using Generative Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104312 titled 'Photorealistic Text Inpainting for Augmented Reality Using Generative Models
Simplified Explanation
The patent application describes systems and methods that use generative models to enable photorealistic text inpainting in augmented reality. One example application is augmented reality translation, where real-world text in a scene can be translated into a different language using a machine-learned generative model to produce an augmented image.
- Generative models, such as generative adversarial networks, are used to enable photorealistic text inpainting in augmented reality.
- The systems can be used for augmented reality translation, where real-world text in a scene can be translated into a different language.
- Users can capture imagery of a real-world scene with text using an image capture device like a camera or smartphone.
- The captured imagery is processed with a generative model to produce an augmented image with the real-world text removed, creating a more realistic result compared to simply blocking the text with a colored box.
Potential Applications
The technology can be applied in various fields such as:
- Augmented reality language translation
- Augmented reality gaming
- Augmented reality education and training
Problems Solved
The technology addresses issues such as:
- Enhancing the realism of augmented reality experiences
- Improving the accuracy and efficiency of text translation in augmented reality
- Providing a seamless way to remove and replace text in real-world scenes
Benefits
The benefits of this technology include:
- Enhanced user experience in augmented reality applications
- Improved accessibility for non-native language speakers
- Increased efficiency in translating and manipulating text in real-time scenarios
Potential Commercial Applications
Potential commercial applications of this technology include:
- Augmented reality language learning apps
- Augmented reality advertising and marketing campaigns
- Augmented reality navigation and tourism guides
Possible Prior Art
One possible prior art could be the use of traditional image editing techniques to remove or replace text in images, which may not achieve the same level of realism as generative models in augmented reality text inpainting.
Unanswered Questions
How does this technology handle complex fonts and languages with different character sets?
The patent application does not specifically address how the generative models handle complex fonts or languages with unique character sets.
What are the limitations of using generative models for text inpainting in augmented reality?
The patent application does not discuss any potential limitations or challenges that may arise when using generative models for text inpainting in augmented reality.
Original Abstract Submitted
provided are systems and methods that use generative models (e.g., generative adversarial networks) to enable photorealistic text inpainting in augmented reality. one example application of the proposed systems is to perform augmented reality translation. for example, a user can operate an image capture device (e.g., camera, smartphone, etc.) to capture imagery of a real-world scene that includes real-world text (e.g., signage, restaurant menus, etc.). the real-world text can be translated into a different language. further, the captured imagery can be processed with a machine-learned generative model to produce an augmented image. the augmented image can depict the real-world scene with the real-world text removed. specifically, because a machine-learned generative model is used, the augmented image can appear significantly more realistic, for example versus an image in which the real-world text has simply been blocked using a box with a single color.
