Synthetic Image Generation Using GANs for E-Commerce
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Solution Overview
Problem
The generation of high-quality photographs, especially for e-commerce and fashion photography, is a time-consuming and costly process requiring physical shipment of products to studios, limiting the variety of images that can be produced due to the need for professional setups and arrangements.
Innovation Solution
The development of systems and techniques for generating synthetic photorealistic images and videos that maintain specific attributes while varying others, using generative adversarial networks (GANs) and machine learning models to create diverse images without physical presence of products or environments, allowing for extensive variability in product arrangements and styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If professional photography studios with proper lighting and equipment are used to generate high quality photographs, then image quality is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent uses generative adversarial networks (GANs) to create synthetic images that copy the visual characteristics and quality of professional photographs without requiring physical photography sessions. The generator model learns from real product images and generates photorealistic synthetic images, eliminating the need for time-consuming studio shoots while maintaining image quality
Solution Approach 2:
The patent replaces the mechanical photography system (cameras, lighting equipment, physical studios) with a computational system based on machine learning models. Instead of physically capturing images through optical equipment, the system uses neural networks to generate images computationally, dramatically reducing time and resource requirements
2Manufacturing precision
If professional photography studios are used for product photography, then high quality images are produced, but device complexity and operational complexity increase
Solution Approach 1:
The patent creates a universal image generation system that can handle multiple product types, styles, and scenarios through a single trained model. The GAN framework serves multiple functions: learning from real images, generating synthetic images, and enabling various attribute manipulations, replacing the need for multiple specialized photography setups
3Reliability
If physical products are shipped to photography studios for imaging, then authentic product representations are achieved, but loss of time and increase in cost occur
Solution Approach 1:
The patent performs preliminary training of the generative model using real product images before actual image generation. The model learns authentic product characteristics, lighting, and textures in advance, enabling it to generate reliable product representations without requiring physical products to be present during the actual image generation process
Solution Approach 2:
The system creates accurate copies of product appearances through synthetic image generation. By learning from real product images during training, the model can generate photorealistic representations that maintain product authenticity without requiring physical products, eliminating shipping and setup time
4Manufacturing precision
If professional photography setups are used, then high quality images are produced, but adaptability and versatility decrease due to limited configurations
Solution Approach 1:
The patent implements a dynamic image generation system where attributes such as background, lighting, product pose, and style can be modified through vector manipulations in the latent space. This allows continuous variation of image configurations while maintaining quality, replacing the static nature of physical studio setups
Solution Approach 2:
The system enables versatile image generation by changing parameters in the latent space of the trained model. By manipulating vectors corresponding to different attributes (background type, lighting conditions, product orientation), the system can generate diverse image configurations from a single trained model, maintaining quality while achieving adaptability
Data Source
AI summary
Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.