Collaborative Virtual Experience Provisioning via AI 3D Generation
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Solution Overview
Problem
Current methods for generating 3D virtual experiences from 2D input data are limited by quality of renders, require manual effort, are time-consuming and error-prone, and are dependent on specialized hardware, and are inefficient for delivery on user devices with limited resources.
Innovation Solution
A method and system for provisioning a collaborative virtual experience that transmits interactive 3D model data to user devices, receives follower state data, and generates collaborative view data, allowing for interactive and efficient creation and presentation of virtual experiences without the need for specialized hardware, using AI and ML algorithms for auto-input and virtual tour creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to generate 3D virtual experiences from 2D input data, then quality of renders can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical processes with automated AI/ML algorithms. The system uses machine learning models to automatically generate 3D virtual experiences from 2D input data, eliminating manual intervention while maintaining high quality renders. The automated processing pipeline includes image processing, 3D model generation, and virtual tour creation without requiring human operators.
Solution Approach 2:
The system changes the operational parameters from manual control to automated algorithmic control. By adjusting parameters such as processing speed, automation level, and quality thresholds, the system achieves both high rendering quality and reduced creation time. The AI models can dynamically adjust parameters like resolution, detail level, and processing intensity to optimize the balance between quality and speed.
2Manufacturing precision
If specialized hardware like 3D depth cameras are used to create virtual experiences, then quality can be improved, but device complexity and cost increase
Solution Approach 1:
The system creates virtual copies of physical spaces using 2D images as input, eliminating the need for specialized capture hardware. Instead of using 3D depth cameras to directly capture spatial data, the system processes 2D photographs through AI algorithms to generate accurate 3D representations. This copying approach maintains quality while significantly reducing hardware complexity.
Solution Approach 2:
The patent replaces expensive, complex specialized hardware with inexpensive, widely available devices. Standard smartphones, tablets, or computers with basic cameras can capture the necessary 2D images, which are then processed by the AI system. This substitution of cheap input devices for expensive specialized hardware reduces device complexity and cost while maintaining output quality.
3Adaptability or versatility
If 3D virtual experiences are transmitted to user devices with limited resources, then accessibility is improved, but transmission time and processing time increase
Solution Approach 1:
The system segments the 3D virtual experience into manageable components and optimizes each for efficient transmission. The virtual tour is divided into individual scenes or segments that can be loaded on-demand rather than transmitting the entire experience at once. This segmentation reduces initial transmission time and allows progressive loading on devices with limited resources.
Solution Approach 2:
The system dynamically adjusts parameters such as resolution, geometry complexity, and texture quality based on the capabilities of the target device. By changing these parameters, the system optimizes the balance between visual quality and transmission/time requirements, enabling efficient delivery to devices with limited processing power, memory, or bandwidth.
4Productivity
If automated AI/ML methods are used to generate 3D virtual experiences, then productivity increases, but system complexity increases
Solution Approach 1:
The system introduces an intermediary AI processing layer between the 2D input images and the 3D output. This intermediary layer, consisting of trained machine learning models, handles the complex transformations automatically. The intermediary absorbs the system complexity, presenting a simple interface to users while managing the sophisticated processing required for high-speed automated 3D generation.
Data Source
AI summary
Disclosed herein is a method of provisioning a collaborative virtual experience to two or more users, in accordance with some embodiments. The method may include transmitting an interactive 3D model data to one or more follower user devices associated with one or more followers. Further, the method may include receiving one or more follower state data from the one or more follower user devices. Further, the method may include generating a collaborative view data based on the interactive 3D model data and the one or more follower state data. Further, the method may include transmitting the collaborative view data to a leader user device operated by a leader associated with the one or more followers. Further, the leader user device may be configured for presenting the collaborative view data.