Diffusion Virtual Try-On Image Generation With Segmentation Filling
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Solution Overview
Problem
Existing systems require significant resources and user effort to create high-quality images of real-world objects in simulated environments, leading to inefficiencies and missed opportunities for sharing and presenting objects effectively.
Innovation Solution
A diffusion model is used to generate photorealistic images of real-world objects wearing target fashion items by analyzing a warped image and segmentation maps, minimizing user interaction and resource expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional methods are used to create high-quality images of real-world objects in simulated environments, then image quality can be improved, but significant resources and user effort are required
Solution Approach 1:
The patent replaces manual mechanical operations (user placing objects, adjusting lighting, positioning) with an automated diffusion model system that generates images through neural network processing. The system substitutes human effort with AI-based image generation that automatically handles object integration, lighting adjustments, and environmental rendering.
Solution Approach 2:
The diffusion model performs self-service by automatically generating high-quality images without requiring user intervention for object placement, lighting adjustment, or environmental setup. The system independently processes input images and generates final outputs, making the image creation process autonomous and eliminating the need for manual fine-tuning.
2Manufacturing precision
If traditional image creation methods are used, then detailed control over image attributes is possible, but user effort and time expenditure increase significantly
Solution Approach 1:
The diffusion model is pre-trained on extensive datasets of images and environmental interactions, allowing it to perform complex image generation tasks without requiring users to manually adjust each parameter. The system has already learned optimal lighting, positioning, and rendering techniques during training, so users only need to provide input images rather than control detailed attributes.
3Adaptability or versatility
If manual methods are used for creating and presenting objects in XR environments, then customization and control are improved, but ease of operation deteriorates due to complex procedures
Solution Approach 1:
The diffusion model serves multiple functions within a single system: it performs object rendering, environmental integration, lighting adjustment, and image generation simultaneously. This multi-functional approach allows users to achieve comprehensive customization without needing to operate separate tools for each task, thereby improving ease of operation while maintaining adaptability.
Data Source
AI summary
Methods and systems are disclosed for generating an extended reality (XR) try-on experience based on an image produced by a diffusion model. The system receives a first image depicting a real-world object and receives a second image depicting a target fashion item. The system generates a warped image in which pixels of the target fashion item depicted in the second image replace pixels of a portion of the real-world object in the first image and generates one or more segmentation maps corresponding to incomplete portions of the warped image. The system analyzes the warped image and the one or more segmentation maps using a generative machine learning model to generate an artificial image that populates the incomplete portions of the warped image to depict the real-world object wearing the target fashion item.