Multi-stage Machine Learning Model Training for AR Stylization
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Solution Overview
Problem
Existing systems for generating high-quality images with augmented reality (AR) items are resource-intensive and time-consuming, requiring expensive equipment and manual adjustments, and machine learning models need extensive training to generate new AR styles.
Innovation Solution
The system trains machine learning models in multiple stages, first on various stylizations to generate AR experiences, and then re-trains using new data for new stylizations, leveraging pre-trained weights to reduce training time and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are trained extensively to generate new AR styles, then the quality and variety of AR experiences improve, but the training time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on a diverse dataset of stylized images across multiple artistic styles before deployment. This pre-training establishes a robust foundation that enables the model to adapt quickly to new AR styles through fine-tuning with minimal additional training data, thereby reducing the time required to generate new styles while maintaining high versatility
Solution Approach 2:
The system utilizes parameter changes by adjusting training hyperparameters such as learning rate, batch size, and training epochs dynamically during the training process. By optimizing these parameters, the model achieves faster convergence and reduced training time while still capturing the full variety of AR styles effectively
2Adaptability or versatility
If machine learning models are trained extensively to generate new AR styles, then the quality and variety of AR experiences improve, but the computational resources and costs increase
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on a diverse dataset of stylized images across multiple artistic styles before deployment. This pre-training establishes a robust foundation that enables the model to adapt quickly to new AR styles through fine-tuning with minimal additional training data, thereby reducing the time required to generate new styles while maintaining high versatility
Solution Approach 2:
The system utilizes copying by using generated stylized images as training data for subsequent training iterations. Instead of requiring extensive new data collection and processing for each new AR style, the model generates synthetic training examples that can be reused and refined, significantly reducing computational resource requirements while maintaining style diversity
3Manufacturing precision
If existing systems generate high-quality images with AR items, then image quality improves, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system replaces manual mechanical processes with an automated machine learning pipeline. Instead of requiring manual image editing, object placement, and AR item integration, the trained machine learning model automatically performs these tasks by processing input images and generating high-quality output images with AR items, thereby maintaining manufacturing precision while dramatically improving productivity
Solution Approach 2:
The system implements self-service through the machine learning model's ability to autonomously perform the complete image generation process. The model automatically identifies objects in input images, selects appropriate AR items, applies stylizations, and generates final high-quality images without human intervention, eliminating resource-intensive manual operations while maintaining production efficiency
Data Source
AI summary
Methods and systems are disclosed for enhancing or modifying an image by a machine learning model. The methods and systems receive an image depicting a real-world object. The methods and systems analyze the image using a machine learning model to generate a modified image that depicts one or more augmented reality stylizations overlaid on the real-world object, the machine learning model trained in multiple stages having different training data sets and different conditions applied in each of the multiple stages. The methods and systems present the modified image on a device.