Neural Network Apparel Image Synthesis for Retail Visualization
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Solution Overview
Problem
Retailers face challenges in providing customers with accurate representations of garments without the option to try them on, leading to increased costs and potential returns due to the need for extensive photo production showing garments on models.
Innovation Solution
A neural network is trained with pairs of images, one showing a garment and the other showing a model wearing it, allowing for the generation of synthetic images of the garment on a model based on user-adjustable parameters such as skin tone, body shape, and pose, enabling customers to visualize how a garment would look on them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If retailers produce extensive photos showing garments on models, then customers can better visualize how garments fit and appear, but production costs increase
Solution Approach 1:
The patent uses neural networks to generate synthetic images that copy the appearance of garments on models without requiring actual photographs. The system learns from training data the relationship between flat garment images and modeled images, then generates synthetic modeled images for new garments, replacing expensive photo production with automated image synthesis
Solution Approach 2:
The patent replaces the mechanical process of photographing garments on physical models with an automated neural network system. Instead of using cameras, models, studios, and photographers, the system uses machine learning algorithms to synthesize images computationally, eliminating the physical photo production infrastructure
2Ease of manufacture
If retailers provide garment images without models, then production costs decrease, but customers cannot accurately envision how garments fit and appear
Solution Approach 1:
The patent performs preliminary training of the neural network system in advance using pairs of flat garment images and modeled images. This preliminary action creates a learned model that can later generate synthetic modeled images from flat garment images alone, providing visualization information without requiring ongoing photo production
3Ease of operation
If extensive photos showing garments on models are produced, then customer satisfaction increases, but the number of returns due to fit issues increases
Solution Approach 1:
The patent enables customers to adjust parameters such as body type, size, and shape in the synthetic image generation process. This allows customers to visualize how garments will fit their specific characteristics, making more informed purchasing decisions and reducing returns due to fit issues
Data Source
AI summary
Neural networks of suitable topology are trained with sets of images, where one image of each set depicts a garment and another pair of images of each set depicts an item of apparel from multiple viewpoints, and a final image of each set depicts a model wearing the garment and the other item of apparel. Once trained, the neural network can synthesize a new image based on input images including an image of a garment and a pair of images of another item of apparel. Quantitative parameters controlling the image synthesis permit adjustment of features of the synthetic image, including skin tone, body shape and pose of the model, as well as characteristics of the garment and other items of apparel.