Simulated Previews for Dynamic Virtual Cameras
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Solution Overview
Problem
The production of Free-View-Point (FVP) broadcasts, such as sporting events, faces challenges with high manual interaction, high computing power requirements, and latency, making it costly and impractical for real-time or nearly live applications.
Innovation Solution
A software system that generates simulated previews of dynamic virtual cameras using local computing resources, allowing producers to visualize and adjust virtual camera settings in real-time with low latency, by receiving virtual camera descriptor data and object tracking data to create virtual camera behavior data for rendering dynamic scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing resources are used for FVP rendering, then rendering quality and capability are improved, but cost and latency increase
Solution Approach 1:
The rendering process is segmented into multiple passes: a low-resolution preview pass that executes quickly on local devices, and a high-resolution final rendering pass that executes on cloud servers. This segmentation allows the system to provide immediate feedback to producers while maintaining the option for high-quality final output, thereby reducing perceived latency without sacrificing rendering quality.
Solution Approach 2:
The system performs partial rendering action by generating low-resolution preview versions of the FVP content locally before complete high-resolution rendering. This partial action provides sufficient information for producers to evaluate and adjust camera parameters in real-time, reducing the need for repeated cloud rendering cycles and thereby reducing overall latency.
2Reliability
If cloud computing resources are used for FVP rendering, then rendering quality and capability are improved, but cost increases
Solution Approach 1:
The rendering workload is segmented between local devices and cloud servers. Routine preview rendering is performed locally using less expensive resources, while cloud resources are reserved for final high-resolution rendering only when needed. This segmentation reduces the frequency and duration of expensive cloud computing operations, thereby reducing cost while maintaining rendering quality.
Solution Approach 2:
The system creates low-resolution copy versions of the FVP content for preview purposes. These copies are sufficient for evaluation and adjustment but require significantly fewer computational resources to generate. By using copies instead of full-resolution renders for iterative development, the system reduces cloud computing costs while maintaining the ability to produce high-quality final output.
3Manufacturing precision
If manual interaction is increased for camera parameter adjustment, then rendering accuracy is improved, but productivity decreases
Solution Approach 1:
The system implements self-service by automatically generating low-resolution preview renders and presenting them to producers for evaluation. This automated preview generation eliminates the need for manual intervention in the rendering process itself, allowing producers to focus on parameter adjustment based on automated feedback, thereby improving productivity without sacrificing precision.
Solution Approach 2:
The system implements a feedback loop where automated low-resolution previews are generated and presented to producers, who then adjust camera parameters based on this feedback. This feedback mechanism enables rapid iterative adjustment of parameters without requiring manual rendering for each adjustment, thereby improving productivity while maintaining the ability to achieve precise camera parameter settings.
4Loss of time
If high computing power is used for real-time FVP rendering, then latency is reduced, but cost increases
Solution Approach 1:
The rendering task is segmented into low-resolution preview generation (performed locally with minimal latency) and high-resolution final rendering (performed on cloud servers). This segmentation allows the system to achieve low latency for the critical preview and evaluation phase without incurring the high costs of sustained high-power cloud computing, as expensive resources are used only when absolutely necessary.
Solution Approach 2:
The system performs partial rendering action by generating low-resolution previews that provide sufficient information for real-time evaluation. This partial action achieves the functional requirement of real-time feedback without requiring full computational power, thereby reducing cost while maintaining acceptable latency for the preview phase.
Data Source
AI summary
The present disclosure includes a method for generating simulated previews of dynamic virtual cameras, the method comprising receiving virtual camera descriptor data, receiving object tracking data, generating virtual camera behavior data based on the virtual camera descriptor data and the object tracking data, the virtual camera behavioral data corresponding to virtual camera parameters for rendering a view, and generating a simulated preview based on the object tracking data and the virtual camera behavioral data.