Snap inc. (20240378763). DIFFUSION MODEL VIRTUAL TRY-ON EXPERIENCE simplified abstract
Contents
DIFFUSION MODEL VIRTUAL TRY-ON EXPERIENCE
Organization Name
Inventor(s)
Avihay Assouline of Tel Aviv (IL)
Jonathan Heimann of Herzliza (IL)
DIFFUSION MODEL VIRTUAL TRY-ON EXPERIENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378763 titled 'DIFFUSION MODEL VIRTUAL TRY-ON EXPERIENCE
The abstract describes a method and system for creating an extended reality try-on experience using a diffusion model. The system takes two images, one of a real-world object and one of a target fashion item, and generates a warped image where the fashion item replaces part of the real-world object. Segmentation maps are created for incomplete portions of the image, which are then analyzed using a generative machine learning model to generate a complete artificial image showing the real-world object wearing the fashion item.
- The system uses a diffusion model to create an extended reality try-on experience.
- It takes two images, one of a real-world object and one of a target fashion item.
- A warped image is generated where the fashion item replaces part of the real-world object.
- Segmentation maps are created for incomplete portions of the image.
- A generative machine learning model is used to populate the incomplete portions and create a complete artificial image.
Potential Applications: - Virtual fitting rooms for online shopping - Augmented reality fashion apps - Virtual reality gaming experiences
Problems Solved: - Enhancing the online shopping experience - Providing a realistic try-on experience without physically trying on clothes
Benefits: - Increased customer engagement - Reduced return rates for online purchases - Enhanced user experience in virtual environments
Commercial Applications: Fashion retailers can use this technology to enhance their online shopping platforms, increase customer satisfaction, and drive sales.
Questions about Extended Reality Try-On Technology: 1. How does this technology improve the online shopping experience? 2. What are the potential limitations of using generative machine learning models in this context?
Frequently Updated Research: Stay updated on advancements in generative machine learning models and their applications in extended reality try-on experiences.
Original Abstract Submitted
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.
