GAN Image Creation for Salient Object Background Staging
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Solution Overview
Problem
Existing image generation systems for product promotion are inefficient and costly, particularly for staging salient objects in e-commerce, as manual image editing and traditional staging methods are time-consuming and expensive.
Innovation Solution
A framework using generative adversarial networks (GANs) for cost-effective and scalable salient object staging, involving salient object detection, domain relevant background selection, and inpainting to generate images and animations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image editing or traditional staging methods are used to stage salient objects, then high-quality images with products in natural settings can be created, but the process becomes expensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical image editing processes with an automated neural network system. The GAN-based model automatically segments salient objects from source images and generates staged background images by combining object masks with background images, eliminating the need for manual photo editing while maintaining high image quality
Solution Approach 2:
The system creates synthetic staged images by copying and combining existing elements: salient objects are extracted from source images and placed onto selected background images. This copying approach generates realistic staged scenes without requiring physical product photography or manual composition
2Manufacturing precision
If manual image editing or traditional staging methods are used to stage salient objects, then high-quality images with products in natural settings can be created, but the process becomes expensive
Solution Approach 1:
The patent replaces expensive manual image editing services with an automated neural network system. The GAN-based model performs object segmentation, background selection, and image composition automatically, eliminating labor costs associated with professional photo editors while maintaining high image quality
Solution Approach 2:
The system performs self-service by automatically completing the entire image staging workflow without human intervention. The neural network autonomously segments objects, selects appropriate backgrounds, generates masks, and composes final staged images, making the process independent of expensive manual services
3Productivity
If traditional staging methods are used for large collections of salient objects and backgrounds, then product promotion images can be created, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-processing source images to extract salient object masks using trained neural networks. These pre-extracted masks and segmented objects are stored and ready for rapid combination with various background images, enabling efficient batch processing of large collections without repeating the segmentation process
Solution Approach 2:
The system efficiently handles large collections by copying pre-processed object masks and combining them with multiple background images through automated composition. This copying and recombination approach allows rapid generation of numerous staged images from a single source image, dramatically improving productivity for large-scale product catalogs
Data Source
AI summary
Disclosed frameworks for generating an image including a salient object and a staged background include extracting a salient object from a source image and applying a generative model to the salient object to generate the image. According to some embodiments, extracting a salient object from a source image involves using salient object detection method to identify the relevant portions of the source image corresponding to the salient object. In some embodiments, the generative model is a generative adversarial network trained using a domain relevant dataset.