Generative AI Virtual Interaction with Outdoor Imagery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for virtual interaction with outdoor imagery lack the ability to provide continuous iteration of design, interaction with design attributes, and real product information, and require substantial technical expertise and time.
Innovation Solution
The method integrates product data within the context of scenarios using generative AI techniques, facilitating virtual interaction and fine-tuning through human feedback-loop interactions, enabling dynamic integration with real-time product data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing solutions use 3D modelling for experience generation, then virtual interaction capability is provided, but it requires substantial technical expertise and time to create 3D models for each product
Solution Approach 1:
The patent uses image generation models to create virtual 3D representations of outdoor spaces and products by copying and transforming 2D images into 3D virtual models. This eliminates the need for manual 3D modeling while maintaining realistic visual representations, directly addressing the time-consuming nature of traditional 3D model creation
Solution Approach 2:
The patent replaces manual mechanical 3D modeling processes with automated AI-based image generation systems. Instead of requiring technical expertise to create 3D models, the system uses generative AI algorithms to automatically transform 2D images into 3D virtual representations, significantly reducing the skill barrier and time investment
2Adaptability or versatility
If existing solutions focus on 3D modelling for experience generation, then virtual interaction is enabled, but they lack ability to provide continuous iteration of design and interaction with design attributes
Solution Approach 1:
The patent implements dynamic design iteration by allowing users to continuously modify virtual space attributes such as lighting conditions, weather effects, product placements, and spatial arrangements. The system responds in real-time to user inputs, enabling flexible and adaptive design exploration without requiring complex manual reconfiguration
Solution Approach 2:
The patent incorporates feedback mechanisms where users can interact with virtual representations, provide feedback on design attributes, and the system iteratively refines the virtual model accordingly. This closed-loop feedback system enables continuous design improvement while maintaining manageable complexity through automated response to user inputs
3Productivity
If existing solutions use mixed reality-based approaches, then object placement modification is possible, but they lack automatic image regeneration and attribute-based interaction
Solution Approach 1:
The patent performs preliminary actions by pre-generating multiple virtual variations of outdoor spaces with different attributes before user interaction. The system pre-creates alternative configurations of lighting, weather, product arrangements, and spatial layouts, allowing users to instantly switch between pre-computed options rather than modifying each attribute manually in real-time
Solution Approach 2:
The patent enables attribute-based interaction by allowing users to modify virtual space parameters such as lighting intensity, weather conditions, product quantities, and spatial positions. The system automatically regenerates images based on these parameter changes, providing efficient and intuitive control over virtual representations without requiring complex manual operations
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
The present disclosure relates to a method (200) for transforming a visual comprises steps of integrating, by way of processing circuitry (120) of an information processing apparatus (106), product data within a context of one or more scenarios to form a modified visual. In particular, the integration is based on human feedback-loop interactions. Moreover, the integration includes utilizing generative Artificial Intelligence (AI) techniques to generate one or more scenarios, fusing product data within generated synthetic scenarios to form modified visual; facilitating virtual interaction with modified visual, and fine-tuning the modified visual using generative AI techniques.