AR Text Inpainting Using Generative Models for Realistic Translation
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Solution Overview
Problem
Existing augmented reality systems struggle to provide realistic and immersive translation of real-world text by simply overlaying or blocking it with a uniform color, leading to difficulty in reading and a loss of immersion due to inconsistent camera poses and dynamic scene changes.
Innovation Solution
Utilizing machine-learned generative models, particularly generative adversarial networks, to remove real-world text and replace it with a visually accurate background, allowing for the integration of translated text in a more realistic and consistent manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple text overlay or uniform color blocking is used, then the implementation is simple and fast, but the visual realism and immersion are poor
Solution Approach 1:
The patent introduces an intermediary inpainting model that generates intermediate pixel data to bridge the gap between simple text removal and realistic background restoration. This intermediary component processes the text-free regions and produces visually coherent background content that matches the surrounding area, resolving the contradiction between implementation simplicity and visual realism.
Solution Approach 2:
The patent replaces traditional mechanical image editing methods (manual inpainting, uniform blocking) with an AI-based generative model. This substitution enables automatic, intelligent background reconstruction that adapts to complex scenes, dramatically improving visual realism while maintaining computational efficiency through optimized model architecture.
2Manufacturing precision
If complex inpainting models are used to restore background, then visual accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the image processing task into distinct components: text detection, text removal, inpainting model processing, and result composition. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining high background restoration accuracy through specialized processing for each stage.
Solution Approach 2:
The patent applies partial inpainting by focusing computational resources only on regions where text was detected and removed, rather than processing the entire image. This partial action approach significantly reduces computational complexity while maintaining high restoration accuracy in the critical text-affected areas.
3Speed
If text is blocked with uniform color, then the processing is fast, but the immersive feeling and reading ability are reduced
Solution Approach 1:
The patent introduces an intermediary inpainting process that generates realistic background content instead of using uniform color blocking. This intermediary step restores the visual continuity and contextual information necessary for maintaining immersion and reading ability, while the overall system remains efficient through optimized processing pipelines.
4Reliability
If simple blocking algorithms are used, then the system is robust to dynamic changes, but the visual quality and consistency deteriorate under varying camera poses and lighting
Solution Approach 1:
The patent implements a dynamic system that adapts to varying camera poses and lighting conditions by using the inpainting model to generate context-appropriate background content. The model learns from training data to understand different scene conditions and produces visually consistent results across diverse environments, maintaining both reliability and visual quality.
Data Source
AI summary
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.