Multimodal Texture Generation for XR Try-On Image Quality
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Solution Overview
Problem
Existing systems require significant user effort and resources to create high-quality images, leading to inefficiencies and missed opportunities for sharing and presenting real-world objects in ideal settings, often resulting in lower quality images that undervalue the objects.
Innovation Solution
The disclosed techniques leverage a multimodal memory to store interaction data and use machine learning models to generate photorealistic images of real-world objects in simulated scenes with minimal user interaction, dynamically generating context-sensitive prompts for artificial textures and fashion items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually create high-quality images with expensive equipment and meticulous adjustments, then image quality is improved, but time consumption and expense increase significantly
Solution Approach 1:
The system enables automatic image generation where the AI model autonomously creates photorealistic images by detecting objects in input images and generating corresponding synthetic images with appropriate textures, lighting, and backgrounds without requiring manual user intervention for each image creation task
Solution Approach 2:
The patent replaces manual mechanical operations (physical photography equipment, manual lighting adjustments, manual object placement) with an AI-based automated system that uses machine learning models to detect objects and generate images computationally, substituting physical image capture processes with digital synthesis
2Manufacturing precision
If users manually adjust lighting and image attributes to enhance object presentation, then image quality is improved, but effort and complexity increase
Solution Approach 1:
The AI system automatically performs lighting adjustment, texture generation, and image attribute optimization by analyzing the detected object and generating appropriate visual parameters, eliminating the need for users to manually adjust these complex settings
Solution Approach 2:
The patent introduces an AI model as an intermediary between the user's simple object detection input and the complex image generation output, where the AI automatically handles the intermediate steps of lighting calculation, texture synthesis, and visual parameter optimization
3Ease of manufacture
If users lack resources to create high-quality images, then resource requirements are reduced, but image quality and object value presentation deteriorate
Solution Approach 1:
The system creates photorealistic synthetic copies of detected objects through AI generation, producing images that visually replicate high-quality photographs without requiring physical photography equipment, models, or studio resources
Solution Approach 2:
The patent replaces expensive physical resources (photography equipment, studio lighting, physical models) with computational resources that run AI models to generate equivalent or superior visual results digitally
4Productivity
If automated image generation is implemented, then productivity and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex image generation task into separate functional modules: an object detection model that identifies objects in input images, a texture generation model that creates appropriate textures, and an image synthesis model that combines elements into final photorealistic images, allowing each component to be optimized independently
Data Source
AI summary
Methods and systems are disclosed for generating an extended reality (XR) try-on experience. The methods and systems store, in a multimodal memory, interaction data representing use of one or more interaction functions including data in different modalities. The methods and systems detect an object depicted in an image captured by an interaction client and generate, by a machine learning model, a prompt based on the object depicted in the image and the interaction data in the multimodal memory. The methods and systems generate an artificial texture based on the prompt and modify a texture of the object depicted in the image using the artificial texture that has been generated based on the prompt.