AI-Generated Backgrounds for Realistic Product Images
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Solution Overview
Problem
Conventional image-editing tools require manual editing to create realistic images with altered backgrounds, which is time-consuming and resource-intensive, and existing methods for generating AI-generated backgrounds often fail the believability test.
Innovation Solution
Utilizing trained AI models to generate AI-generated backgrounds for images of items, with techniques for preprocessing captured images to reduce resource usage and enhance realism, allowing users to create professional-grade images without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image editing is used to create realistic images with altered backgrounds, then image quality can be improved, but time consumption and resource usage increase
Solution Approach 1:
The patent replaces manual image editing operations with an AI-based automated system. The trained neural network model automatically generates realistic backgrounds and composites images without requiring manual intervention, thereby maintaining high image quality while significantly reducing time consumption and resource usage compared to traditional manual editing processes
Solution Approach 2:
The system creates synthetic background images and composite images that replicate realistic scenarios without requiring physical travel or manual photography. The AI model generates virtual copies of desired backgrounds (e.g., beaches, forests, urban settings) that can be applied to product images instantly, eliminating the need for photographers to travel to remote locations
2Manufacturing precision
If photographers travel to remote locations to capture images in desired settings, then image realism is improved, but resource consumption and time requirements increase
Solution Approach 1:
The patent replaces the physical process of traveling to remote locations with a computational AI system. The trained neural network generates photorealistic backgrounds virtually, substituting the need for photographers to physically transport themselves and equipment to distant locations, thereby maintaining image realism while eliminating the associated resource consumption and time costs
Solution Approach 2:
The system creates virtual copies of real-world environments through AI generation. Instead of physically traveling to capture images in desired settings, the system generates synthetic representations of those environments (beaches, forests, urban scenes) that indistinguishably resemble real photographs, thereby achieving the same realism without the resource expenditure of travel
3Adaptability or versatility
If conventional image editing tools are used, then background alteration is possible, but the process becomes complex and resource-intensive
Solution Approach 1:
The patent replaces complex manual image editing tools with an AI-based automated system. The trained neural network automatically performs background generation, segmentation, and composition tasks that would otherwise require sophisticated manual editing software and skilled operators, thereby maintaining versatile background alteration capability while significantly reducing process complexity
Solution Approach 2:
The AI system performs self-service by automatically generating and applying backgrounds without requiring user intervention in the complex editing process. The trained model independently handles image processing tasks such as segmentation, background generation, and composite creation, eliminating the need for users to navigate complex editing interfaces and commands
Data Source
AI summary
Generating images of items with artificial intelligence (AI)-generated backgrounds is described. An example process includes capturing an image(s) of an item, obtaining first image data that identifies a real-world background in the image(s), and presenting a user interface for a user of an electronic device to indicate a descriptor(s). The process may further include receiving, via the user interface, an indication of the descriptor(s), generating prompt data representing a prompt(s) based at least in part on the descriptor(s), sending the first image data and the prompt data to a server computer(s), receiving, from the server computer(s), second image data output by a trained AI model(s), the second image data representing an AI-generated image(s) with an AI-generated background(s), and presenting, based at least in part on the second image data, a candidate image(s) with the AI-generated background(s) for selection by the user.