18584777. LOCAL ATTRIBUTE IMAGE EDITING USING AN IMAGE GENERATION MODEL AND A FEATURE IMAGE GENERATION MODEL simplified abstract (TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED)
Contents
LOCAL ATTRIBUTE IMAGE EDITING USING AN IMAGE GENERATION MODEL AND A FEATURE IMAGE GENERATION MODEL
Organization Name
TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Inventor(s)
LOCAL ATTRIBUTE IMAGE EDITING USING AN IMAGE GENERATION MODEL AND A FEATURE IMAGE GENERATION MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18584777 titled 'LOCAL ATTRIBUTE IMAGE EDITING USING AN IMAGE GENERATION MODEL AND A FEATURE IMAGE GENERATION MODEL
The abstract describes an image editing method that involves acquiring initial image generation and feature image generation models, training them based on different training image sets, and fusing the output images to obtain a reference object image.
- The method involves training initial and feature image generation models on different training image sets.
- A joint mask image is acquired based on image regions corresponding to the target attribute in object images.
- Second initial and feature object images are obtained using target network layers.
- The second initial and feature object images are fused based on the joint mask image to obtain a reference object image.
Potential Applications: - Image editing software - Graphic design tools - Photography applications
Problems Solved: - Enhancing image quality - Improving image editing efficiency
Benefits: - Enhanced image editing capabilities - Streamlined editing process
Commercial Applications: Title: Advanced Image Editing Technology for Graphic Design Software This technology can be used in graphic design software to improve image editing capabilities, attracting more users and potentially increasing revenue for companies in the graphic design industry.
Prior Art: Prior art related to this technology may include research papers on image editing methods and machine learning models for image processing.
Frequently Updated Research: Researchers are constantly exploring new techniques for image editing and machine learning models to enhance image generation and editing processes.
Questions about Image Editing Technology: 1. How does this technology improve upon existing image editing methods? 2. What are the potential limitations of using machine learning models for image generation and editing?
Original Abstract Submitted
An image editing method includes acquiring an initial image generation model and a feature image generation model, the initial image generation model having been trained based on a first training image set, the feature image generation model having been obtained by training the initial image generation model based on a second training image set. The method further includes acquiring a joint mask image based on image regions corresponding to the target attribute in the object images, and acquiring a second initial object image and a second feature object image output by corresponding target network layers. The method further includes fusing the second initial object image and the second feature object image based on the joint mask image to obtain a reference object image.
