Real-Time Virtual 3D Reconstruction of Live Scenes
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Solution Overview
Problem
Conventional live broadcasts lack interactivity and fail to provide realistic, real-time virtual 3D reconstruction of live scenes, especially in sports events, due to limitations in tracking systems that can only provide one positional coordinate per character or object, leading to delayed and abstracted animations.
Innovation Solution
A method for real-time virtual 3D reconstruction using 3D positional tracking data, which involves event detection, analysis, and the selection of pre-defined animation data sets stored in a database, allowing for immediate and interactive visualization of live scenes by analyzing positional data and adjusting animation data sets in real-time to match the live event, incorporating both simulated and motion-capture data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional tracking systems are used to provide positional data for live scenes, then the system complexity is reduced, but the animation realism and interactivity deteriorate due to limited positional coordinates and delayed reconstruction
Solution Approach 1:
The system segments the live scene into multiple tracked objects and characters, each with its own positional tracking data. This allows complex scenes to be broken down into manageable components that can be independently processed and animated, improving animation realism without overwhelming the tracking system
Solution Approach 2:
The system performs preliminary action by pre-defining event characteristics and animation data sets before live reconstruction. Event templates and animation libraries are prepared in advance, enabling rapid matching and instantiation during live events, thus achieving high realism without requiring complex real-time generation
2Productivity
If real-time event detection and animation selection are implemented, then the interactivity and responsiveness of live reconstruction are improved, but the processing time and computational complexity increase
Solution Approach 1:
Event characteristics, animation data sets, and matching criteria are all prepared in advance. During live reconstruction, the system only needs to compare incoming positional data against pre-defined templates and select the best matching animation, dramatically reducing real-time computational requirements while maintaining high productivity
Solution Approach 2:
The system uses copying by selecting from pre-defined animation data sets that replicate realistic motion patterns. Instead of generating animations from scratch in real-time, the system copies and instantiates pre-animated sequences that match detected events, achieving high-speed reconstruction with complex visual realism
3Adaptability or versatility
If multiple animation data sets are used to represent different events, then the versatility and coverage of live scene reconstruction are improved, but the data storage requirements and system complexity increase
Solution Approach 1:
The animation library is segmented into distinct data sets categorized by event type, character, and action. This organized segmentation allows the system to load and process only relevant animation subsets for each specific event, managing large volumes of animation data efficiently while maintaining comprehensive event coverage
Solution Approach 2:
Animation data sets are designed with universal structures that can represent multiple event types and scenarios. By using parameterized animation templates that can be instantiated with different parameters, the system achieves high versatility with compact data storage, avoiding the need for completely separate animation files for each possible event
Data Source
AI summary
A method, system and computer-program product for real-time virtual 3D reconstruction of a live scene in an animation system. The method comprises receiving 3D positional tracking data for a detected live scene by the processor, determining an event by analyzing the 3D positional tracking data by the processor, comprising steps of determining event characteristics from the 3D positional tracking data, receiving pre-defined event characteristics, determining an event probability by comparing the event characteristics to the pre-defined event characteristics, and selecting an event assigned to the event probability, determining a 3D animation data set from a plurality of 3D animation data sets assigned to the selected event and stored in the data base by the processor, and providing the 3D animation data set to the output device.