AR/VR Headset Voice-to-3D Generation via Microservice Segmentation
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Solution Overview
Problem
Current voice-controlled 3D image generation in AR/VR environments lacks efficient integration of voice-to-text and text-to-3D conversion technologies, limiting the ability to generate high-quality 3D objects and scenes in real-time responsive to user verbal requests.
Innovation Solution
A system integrating voice-to-text functionality with text-to-2D image generation using diffusion models and 2D-to-3D conversion models, such as Stable DreamFusion and DeepFloyd IF, to generate high-fidelity 3D models within the user's AR/VR session, utilizing microservices and machine learning technologies like Python and Flask for asynchronous data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If voice-to-text and text-to-3D conversion technologies are integrated for real-time 3D object generation in AR/VR, then user interaction efficiency and experience are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the 3D content generation process into distinct microservices: voice-to-text conversion service, text-to-2D image generation service, and 2D-to-3D conversion service. Each microservice handles a specific transformation task independently, allowing parallel processing and reducing overall system complexity while maintaining high user interaction efficiency.
Solution Approach 2:
The patent introduces intermediate representations in the processing chain: voice commands are first converted to text prompts, then to 2D images, and finally to 3D objects. These intermediate forms act as mediators that bridge different processing stages, enabling efficient transformation while managing system complexity through standardized intermediate formats.
2Manufacturing precision
If high-fidelity 3D models are generated from text prompts using diffusion models, then image quality and realism are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary 2D image generation from text prompts using diffusion models before converting to 3D. This preliminary action creates a high-quality intermediate representation that guides the subsequent 3D generation process, ensuring final 3D models inherit the quality and detail from the pre-generated 2D images while reducing the computational burden of direct text-to-3D conversion.
Solution Approach 2:
The patent employs a dimensionality transition approach by converting text prompts to 2D images first, then transforming those 2D representations into 3D models. This two-dimensional intermediate step facilitates more efficient and higher-quality 3D generation compared to direct text-to-3D conversion, as the 2D image provides a detailed visual reference that guides the 3D reconstruction process.
Data Source
AI summary
A system for generating and providing an object in the field of view (FOV) of a user's augmented reality or virtual reality (AR/VR) session responsive to a verbal request for the object is described herein. An AR/VR communication component receives a verbal request for an object from the user and produces a text request for the object based on the verbal request. 2D image and 3D model generation components generate a 3D model of the object which is provided within the FOV of the user's AR/VR session.