Contextual Image Resizing and Filling for Custom Product Mockups
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Solution Overview
Problem
Current systems lack efficient methods for generating custom images in real-time with high quality and accuracy, particularly for custom products, and often rely on stock images that cannot be modified or customized by users, leading to a suboptimal user experience.
Innovation Solution
Utilizing AI-based tools to generate synthetic images on-the-fly that match the content and context of custom products, filling in empty areas with images that are uniquely generated and not copied from existing repositories, using techniques like neural networks and stable diffusion models to ensure coherence and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If stock images are used as backgrounds for displaying custom products, then the system can provide pre-prepared images, but users cannot modify or customize them and the user experience is not enhanced
Solution Approach 1:
The system enables users to customize backgrounds by allowing them to upload their own images or select from templates, and automatically generates synthetic views combining the custom product with the chosen background. This self-service approach eliminates the limitation of fixed stock images while maintaining ease of use through automated processing.
Solution Approach 2:
The background images transition from static pre-prepared stock images to dynamic customizable backgrounds that can be modified in real-time. The system dynamically generates synthetic views based on user selections, allowing backgrounds to adapt to different customization scenarios while maintaining rendering efficiency through optimized processing pipelines.
2Manufacturing precision
If synthetic images are generated in real-time based on custom product information, then image quality and accuracy are improved, but processing time may increase
Solution Approach 1:
The system performs preliminary processing by pre-computing lighting conditions, material properties, and geometric transformations of custom products. These pre-computed data structures are stored and reused during real-time rendering, enabling high-quality synthetic image generation without significant processing delays.
Solution Approach 2:
The system replaces traditional physics-based rendering with AI-based image generation models that can produce photorealistic synthetic images faster. Neural networks learn from training data to generate accurate product representations without requiring computationally expensive physical simulations, thus maintaining image accuracy while reducing processing time.
Data Source
AI summary
In some embodiments, a method for generating digital image extensions contextually based on a custom image comprises: determining a plurality of image elements in a custom image based on one or more characteristics associated with image elements of the plurality of image elements; determining a plurality of product components of a custom product; for each product component: selecting one or more first image elements based on element characteristics associated with the one or more first image elements; based on the product component and the one or more first image elements, generating a first request for customizing the product component of the custom product; transmitting the first request to an AI-based image generator to cause the AI-based image generator to generate a first customized synthetic images for the product component; including the first customized synthetic images in a region corresponding to the product component of the custom product.