Video-to-Virtual Content Generation for Multi-Style 3D Environments
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Solution Overview
Problem
Conventional methods for generating virtual environments using 3D computer graphics require significant costs and efforts to change graphic styles, making it difficult to provide varied virtual environments according to user preferences.
Innovation Solution
A method and system that convert video content into virtual environments by extracting and modifying motion data using machine learning models, allowing generation of virtual content with different graphic styles efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional 3D computer graphics methods are used to generate virtual environments, then the graphic quality and realism can be maintained, but the cost and effort required to change graphic styles increases significantly
Solution Approach 1:
The patent uses video content as a copy or reference source to generate virtual environments. Instead of creating 3D models from scratch, the system extracts motion data from video footage and uses it to animate virtual objects, thereby copying the motion patterns from real-world video to virtual environments. This eliminates the need for manual 3D modeling while maintaining realistic motion representation.
Solution Approach 2:
The patent replaces the mechanical 3D modeling process with an automated data extraction and conversion system. Machine learning models automatically extract motion data from video content and convert it into virtual environment assets, substituting the manual mechanical process of 3D modeling with an automated computational pipeline that reduces both time and complexity.
2Productivity
If manual 3D modeling is performed to create virtual environments, then the precision and quality of virtual content can be ensured, but the time and resources required increase significantly
Solution Approach 1:
The patent performs preliminary action by extracting motion data from video content before the actual virtual environment generation. The machine learning models pre-process the video footage to extract relevant motion patterns, which are then directly applicable to animating virtual objects. This preliminary extraction eliminates the need for time-consuming manual animation work during the virtual environment creation phase.
Solution Approach 2:
The system enables self-service by allowing video content to automatically generate the motion data needed for virtual environments. The machine learning models autonomously analyze video footage and produce animated virtual content without requiring manual intervention for each animation task. The process serves itself by using the input video to generate the necessary motion parameters.
3Adaptability or versatility
If multiple virtual environments with different graphic styles are created using conventional methods, then user preferences can be satisfied, but the costs and efforts multiply significantly
Solution Approach 1:
The patent implements universality by creating a single automated system that can generate multiple virtual environments with different graphic styles from the same video content. The machine learning models are designed to be style-agnostic, allowing the same extraction pipeline to produce various graphic styles by adjusting parameters or applying different rendering approaches, thereby making the system multi-functional for diverse virtual environment creation.
Data Source
AI summary
A method for generating virtual content is provided, which is performed by one or more processors, and includes receiving video content, extracting first motion data of a first object included in the video content, and converting the video content in accordance with a first virtual environment based on the extracted first motion data of the first object so as to generate virtual content in the first virtual environment.