AR Effect Description Generation Using Visual Image Comparison
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Solution Overview
Problem
Existing interaction systems face challenges in automating the generation of accurate and relevant descriptions for a large number of AR effects, as current object recognition techniques struggle to distinguish between AR effects and base content, leading to limited usefulness in search, indexing, and classification tasks.
Innovation Solution
Utilizing a visual-semantic machine learning model to analyze concatenated images before and after applying AR effects, generating detailed and granular descriptions by comparing the visual transitions, and employing prompts to extract natural language descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current object recognition techniques are used to generate AR effect descriptions, then the process can be automated, but the descriptions are inaccurate and fail to distinguish between AR effects and base content
Solution Approach 1:
The patent segments the image processing into distinct stages: first applying the AR effect to generate an augmented image, then separately analyzing the augmented image and the original image through visual question answering to extract specific effect descriptions. This segmentation allows the system to focus on identifying only the AR effect components rather than describing the entire image, thereby improving accuracy while maintaining automation.
Solution Approach 2:
The patent introduces visual-semantic machine learning models as intermediaries between the AR effect application and the description generation process. These models act as mediators that process both the augmented and original images through VQA prompts, enabling accurate extraction of AR effect descriptions by comparing visual differences without requiring manual analysis.
2Measurement precision
If manual review and tagging of AR effects is performed, then description accuracy improves, but the process becomes time-consuming and inefficient for large numbers of effects
Solution Approach 1:
The system enables self-service by allowing the visual-semantic machine learning models to automatically generate accurate AR effect descriptions without human intervention. The models autonomously process image pairs, answer visual questions, and produce descriptions, eliminating the need for manual review while maintaining high accuracy through the comparative analysis methodology.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated visual-semantic machine learning system. Instead of human operators manually reviewing and tagging AR effects, the system uses VQA-based machine learning models that automatically analyze image differences and generate descriptions, significantly improving productivity while maintaining accuracy.
3Device complexity
If general object recognition models are used, then the system is simple to implement, but the descriptions lack granularity and detail about specific AR effects
Solution Approach 1:
The patent employs dynamic prompting strategies where the visual question answering prompts are adapted based on the specific AR effect being analyzed. The system uses different VQA prompts tailored to extract specific effect characteristics, allowing the description generation process to dynamically adjust to the unique features of each AR effect while maintaining a unified automated framework.
Solution Approach 2:
The system changes the parameters of the visual-semantic machine learning model by adjusting the VQA prompts and analysis focus based on the AR effect type. By modifying the questioning parameters and analysis depth, the system extracts detailed and granular effect descriptions without requiring fundamentally different models for each effect type, balancing complexity with information quality.
Data Source
AI summary
A first image and a second image are accessed. The second image is generated by applying an augmented reality (AR) effect to the first image. The first image, the second image, and a prompt are provided to a visual-semantic machine learning model to obtain output describing at least one feature of the AR effect. A description of the AR effect is generated based on the output of the visual-semantic machine learning model. The description of the AR effect is stored in association with an identifier of the AR effect.