2D Image Recommendation from 3D Model Viewing Data
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Solution Overview
Problem
Merchants struggle to select the most appropriate image for their online store listings and search engine responses, as current methods lack a systematic approach to determine the representative image that maximizes engagement and attractiveness.
Innovation Solution
The system recommends 2D images based on interactions with 3D models within augmented or virtual reality environments, utilizing user activity data to identify and select images that best represent products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a merchant manually selects a representative image for online store listings, then the process is simple and quick, but the selected image may not maximize customer engagement or accurately represent the product
Solution Approach 1:
The system automatically analyzes customer interactions with 3D models and autonomously selects the most representative 2D images without requiring merchant intervention. The system serves itself by using interaction data to make intelligent image selection decisions, resolving the contradiction between operational simplicity and selection accuracy.
Solution Approach 2:
The system uses feedback from customer interaction data with 3D models to continuously improve image selection. By monitoring how customers manipulate and view 3D models, the system learns which views are most engaging and uses this feedback to automatically select optimal 2D images for listings.
2Adaptability or versatility
If multiple images are provided for a product listing, then customers have more options to view the product, but determining which image should be the default becomes more complex
Solution Approach 1:
The system automatically determines the default image from multiple options by analyzing customer interaction patterns with 3D models. This self-service approach eliminates the need for complex manual selection processes while maintaining adaptability to customer preferences.
Solution Approach 2:
The system changes the selection criterion from subjective merchant preference to objective customer interaction metrics. By using interaction parameters (viewing time, manipulation patterns, zoom levels) as the basis for selection, the system simplifies the decision process while improving adaptability to customer needs.
3Productivity
If the default image is optimized for customer engagement, then customer interest and virtual foot traffic increase, but the process of selecting the optimal image becomes more complex
Solution Approach 1:
The system uses real-time feedback from customer interactions with 3D models to automatically identify engaging image views. This feedback loop enables the system to optimize for customer engagement without manual intervention, resolving the contradiction between improved productivity and increased system complexity.
Solution Approach 2:
The system introduces customer interaction data as an intermediary between the product and the image selection process. This intermediary provides objective metrics that guide image selection, enabling automated optimization of customer engagement without requiring complex merchant decisions.
Data Source
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AI summary
Methods and systems for generating 2D images based on a 3D model are disclosed. 3D three-dimensional (3D) model data associated with a product offering in an online store are sent to a first electronic device, the 3D model data being generated from a stored 3D model. Data representing a selected value for a viewing parameter of the stored 3D model are received from the first electronic device. From the received data, a desired 2D view is determined for a stored 3D model. A recommendation is generated, for a second electronic device, to include the desired 2D view in a stored listing associated with the product offering.