Latent Vector Stream Renderer for Real-Time Image Editing
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Solution Overview
Problem
Conventional digital image editing systems using generative adversarial networks (GANs) are computationally inefficient, requiring excessive processing resources and limiting real-time editing capabilities, especially on less powerful devices like mobile devices, due to their need for generating and transmitting entirely new images for each edit operation.
Innovation Solution
A latent vector image editing system that detects user interactions on a client device and modifies digital images using a web-based intermediary, generating an image delta (difference metric) to update the image efficiently, allowing for real-time rendering on devices with limited resources by using a latent vector stream renderer and an image modification neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional GAN-based image editing systems generate entirely new digital images for each editing operation, then the image modification can be achieved, but the computing resources and processing time required become excessive
Solution Approach 1:
The patent extracts only the necessary modification information (image delta) rather than regenerating the entire image. The system separates the original image representation from the modification data, transmitting only the delta information to the client device to perform local rendering, thereby reducing computational resource consumption while maintaining image modification quality
Solution Approach 2:
The patent segments the image into original image data and modification delta data. Instead of transmitting and processing complete images for each edit operation, the system divides the computation into: (1) server-side extraction of modification deltas using neural networks, and (2) client-side application of deltas to original images, reducing overall computational burden
2Manufacturing precision
If conventional systems generate entirely new digital images for each edit operation, then the image editing function is achieved, but the processing speed becomes too slow for real-time interaction
Solution Approach 1:
The system performs preliminary extraction of the original image and its latent representation once, then stores this information on the client device. Subsequent edit operations only require processing the modification deltas rather than regenerating images from scratch, enabling real-time interactive editing speeds
Solution Approach 2:
The patent introduces a latent vector representation as an intermediary between the original image and the modification operation. The neural network operates on this intermediate latent space to compute efficient deltas, which then guide the rendering process, significantly improving processing speed while maintaining editing quality
3Adaptability or versatility
If conventional GAN-based image editing systems are deployed on less powerful devices, then device accessibility is improved, but the computational requirements prevent real-time editing
Solution Approach 1:
The server acts as an intermediary that performs the computationally intensive neural network operations to extract modification deltas. This offloads heavy computation from client devices, allowing less powerful devices to access real-time image editing functionality without requiring high local computing power
Solution Approach 2:
The system performs only the necessary partial computation (extracting deltas from original images) on the server side, leaving the actual image rendering and display to the client device. This partial action approach enables accessibility on devices with limited computational resources while maintaining functionality
4Manufacturing precision
If conventional systems require multiple user interactions to manually select and edit portions of digital images, then precise editing control is achieved, but the computing resources and time for processing increase excessively
Solution Approach 1:
The system implements feedback mechanisms where the neural network automatically determines modification deltas based on user selections. Once a user selects a region or attribute, the system provides immediate feedback by computing the appropriate delta and applying it, reducing the number of required interactions and processing time while maintaining precise editing control
Data Source
AI summary
The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.