Drag-Based Image Editing Using Optical Flow and Diffusion Models
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Solution Overview
Problem
Conventional drag-based image editing methods require significant computational resources and memory, necessitate additional user input like text or masks, and are not optimized for real-time high-quality editing.
Innovation Solution
An image editing apparatus and method utilizing a two-stage AI model architecture, comprising a GAN-based optical flow generation module (FlowGen) and a diffusion model-based image generation module (FlowDiffusion), which processes drag inputs to generate optical flow and edit images without additional optimization, reducing computational load and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional drag-based image editing methods are used, then realistic motion adjustment is achieved, but computational resources and memory consumption increase significantly
Solution Approach 1:
The system performs optimization and training beforehand for each individual image to learn realistic motion patterns. This preliminary action stores the computational work in advance, allowing the actual drag-based editing to execute quickly without requiring heavy real-time computation, thus resolving the contradiction between achieving realistic motion and reducing computational resource usage during operation.
2Reliability
If conventional drag-based image editing methods are used, then realistic motion adjustment is achieved, but additional user input (text or masks) is required
Solution Approach 1:
The system automatically learns and determines motion patterns for each individual image through pre-trained models, eliminating the need for users to manually input text descriptions or create masks. The image itself provides the information needed for realistic motion adjustment, making the system self-sufficient and significantly easier to operate while maintaining high reliability.
3Manufacturing precision
If conventional drag-based image editing methods are used, then image editing quality is maintained, but processing speed decreases
Solution Approach 1:
The system performs optimization and training in advance for each image to capture realistic motion patterns. This preliminary computation stores the complex processing work beforehand, enabling the actual drag-based editing to execute rapidly without compromising image quality. The pre-learned models allow real-time or near-real-time editing while maintaining high manufacturing precision.
4Reliability
If conventional drag-based image editing methods are used, then realistic motion is achieved, but memory consumption increases
Solution Approach 1:
The system performs optimization and training beforehand for each individual image, storing the learned motion patterns in pre-trained models. This preliminary action reduces the need for large memory allocations during actual editing operations, as the complex computations have already been performed and stored. The system only needs to load and apply the pre-learned models, significantly reducing memory consumption while maintaining realistic motion capability.
Data Source
AI summary
Proposed are an image editing apparatus and method. According to an embodiment, the image editing apparatus includes: an input/output interface configured to obtain a drag input instruction and an image; and a controller configured to obtain an optical flow based on the drag input instruction and the image by using a first artificial intelligence model that is trained to receive a drag input instruction and an image as input and output an optical flow, to input the optical flow and the image to a second artificial intelligence model that is different from the first artificial intelligence model, thereby obtaining an edited image as an output of the second artificial intelligence model, and to provide the edited image.