3D Scene Semantics for Context-Aware Asset Recommendations
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Solution Overview
Problem
The existing methods for creating 3D environments in virtual reality (VR) are cumbersome and inefficient, requiring users to manually search through vast libraries of 3D assets, which is time-consuming and unintuitive, and lack tools for providing relevant object recommendations based on real-time changes in the environment.
Innovation Solution
A system that uses machine learning to analyze the 3D environment and provide semantics-based recommendations for relevant 3D objects, allowing users to interactively build and customize 3D scenes through an intuitive interface, using natural language descriptions and context-aware object suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search through vast libraries of 3D assets using traditional search methods, then they can find 3D objects, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically analyzes the 3D environment and generates context-aware recommendations without requiring manual user input. The machine learning model autonomously identifies relevant objects, filters assets from the library, and presents recommendations, allowing the system to serve itself rather than requiring continuous user interaction for each search task.
Solution Approach 2:
The patent replaces traditional mechanical search interfaces (keyboard typing, menu navigation, manual browsing) with an intelligent system that uses machine learning to automatically understand the 3D environment context and retrieve relevant assets. This substitution eliminates the need for users to manually interact with search fields and navigate through vast libraries.
2Ease of operation
If users type search queries and navigate menus to find 3D assets, then they can locate objects, but their focus shifts from creative design to search tasks
Solution Approach 1:
The patent extracts the search and retrieval functionality from the user's direct control and transfers it to an automated machine learning system. The complex search interface, filtering logic, and asset matching algorithms are removed from the user's workflow, leaving only the essential creative task of reviewing and selecting from pre-generated recommendations.
Solution Approach 2:
The machine learning system acts as an intermediary between the user's creative intent and the vast 3D asset library. Instead of users directly interacting with the complex library interface, the ML model mediates by understanding the environment context, translating it into appropriate search criteria, and presenting filtered results that align with the creative vision.
3Measurement precision
If traditional search methods are used without context awareness, then users can find assets by keyword, but they cannot efficiently find objects relevant to the current 3D scene
Solution Approach 1:
The system performs preliminary analysis of the 3D environment before the user initiates a search. By continuously monitoring and understanding the context of objects already placed in the scene, the system pre-computes relevant asset recommendations that are contextually appropriate, eliminating the need for users to manually consider relevance criteria during the search process.
Solution Approach 2:
The system uses feedback from the 3D environment itself (the objects already present, their spatial relationships, and the overall scene context) to automatically adjust and refine asset recommendations. This feedback loop allows the system to understand what type of assets would be relevant based on the emerging theme and context of the scene being created.
Data Source
AI summary
Systems and methods are described for providing for display a user interface to facilitate creation of a three-dimensional (3D) environment. The disclosed techniques may generate the 3D environment based on one or more inputs received via the user interface, and perform visual processing of the 3D environment to obtain a natural language description of the 3D environment. The disclosed techniques may determine a context of the 3D environment based at least in part on the natural language description and may store the context in a data structure. A 3D content library may be queried, based on the natural language description of the 3D environment, to identify at least one recommended 3D object that is relevant to the stored context of the 3D environment. The disclosed techniques may providing for display, at the user interface, selectable option(s) to add the at least one recommended 3D object to the 3D environment.