Personalized AR Image Templates Using Generative Model Adaptation
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Solution Overview
Problem
Traditional systems for creating content augmentations in augmented reality (AR) rely on manual design and pre-defined templates, which are time-consuming, resource-intensive, lack diversity, and fail to adapt to individual user preferences, leading to user fatigue and decreased engagement.
Innovation Solution
A generative machine learning model, such as a stable diffusion model, dynamically generates custom image templates tailored to individual users, allowing for real-time adaptation to new users and preferences, reducing time and resource requirements while increasing diversity and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual design and pre-defined templates are used for creating content augmentations, then design control and consistency are maintained, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service content generation by allowing users to input personal characteristics (e.g., facial features, preferences) and automatically generating customized AR content augmentations without requiring manual design intervention. The generative model autonomously creates personalized templates based on user inputs.
Solution Approach 2:
The patent replaces the mechanical manual design process with an automated machine learning system. Instead of designers manually creating templates, a generative model processes user data and automatically generates personalized content augmentations, substituting human creative labor with computational processes.
2Adaptability or versatility
If pre-defined templates are used for content augmentations, then resource requirements are reduced, but diversity and adaptability to individual users are limited
Solution Approach 1:
The system applies local quality by customizing AR content augmentations to match individual user characteristics such as facial features, preferences, and behavioral patterns. Each user receives tailored content rather than generic templates, with modifications localized to their specific attributes while maintaining overall system resource efficiency.
Solution Approach 2:
The generative model dynamically adjusts content parameters (visual characteristics, style, composition) based on user input data. By changing these parameters according to individual user profiles, the system achieves high adaptability without requiring completely separate resources for each user, instead modifying existing template parameters.
3Productivity
If manual design processes are used, then quality control is maintained, but productivity and scalability are reduced
Solution Approach 1:
The patent replaces manual quality control processes with automated machine learning-based generation and validation. The system maintains quality through algorithmic consistency and user feedback loops rather than human review, enabling scalable productivity without sacrificing content quality standards.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with generated content inform subsequent generation iterations. This feedback loop allows the model to learn from user responses and continuously improve content quality while maintaining high productivity through automated processing.
Data Source
AI summary
Described is a system for dynamically applying model adaptations customized for individual users by detecting an image of a first real-world object from a camera feed, detecting landmarks on the first real-world object, and processing the landmarks on the first real-world object using a generative machine learning model to generate a first custom image template for the first real-world object where portions of the first custom image template are populated with visual content placed based on the first custom image template. The system then applies a content augmentation based on the first custom image template to the camera feed.