Virtual Element Animation with Machine Learning for Lower Physics Load
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Solution Overview
Problem
The increasing computational complexity in video games due to numerous virtual elements and interactions leads to processor overload, resulting in input lag, reduced frame rates, and potential damage from high operating temperatures, affecting the gameplay experience.
Innovation Solution
Utilize a machine learning model to generate animation data for virtual elements, reducing the computational burden on processors by mimicking the results of physics engines for simpler interactions, while reserving complex calculations for physics engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics engines are used to model physical interactions for all virtual elements, then physical interaction accuracy is improved, but computational burden increases
Solution Approach 1:
The system segments virtual elements into two categories: those requiring physics engine modeling and those that can use pre-generated animation data. This segmentation allows the physics engine to focus only on elements where physical interaction accuracy is critical, while other elements use lighter-weight animation data, thereby reducing overall computational burden while maintaining necessary accuracy.
Solution Approach 2:
Animation data is generated in advance during an offline training phase using the physics engine, then stored for reuse during gameplay. This preliminary action transfers computational work from the runtime environment to an offline setting, reducing real-time computational burden while preserving physical interaction accuracy when needed.
2Measurement precision
If processors perform more complex calculations to model physical interactions, then physical interaction accuracy is improved, but operating temperature increases
Solution Approach 1:
The system performs complex physics-based calculations during an offline training phase to generate animation data, then stores this data for reuse during gameplay. This shifts the heat-generating computational work from runtime to an offline setting, reducing processor operating temperature during actual gameplay while preserving physical interaction accuracy through the pre-computed animation data.
3Measurement precision
If physics engines model all physical interactions, then physical interaction accuracy is improved, but input lag increases
Solution Approach 1:
The system segments virtual elements into those requiring real-time physics engine processing and those that can use pre-generated animation data. This segmentation reduces the number of elements requiring real-time physics calculations, thereby reducing input lag while maintaining physical interaction accuracy for elements where it is critical.
Solution Approach 2:
Animation data is generated in advance during an offline training phase, then stored for rapid retrieval during gameplay. This preliminary action eliminates real-time physics calculations for many elements, significantly reducing input lag while preserving physical interaction accuracy through the pre-computed data.
4Measurement precision
If physics engines model all physical interactions, then physical interaction accuracy is improved, but frame rate decreases
Solution Approach 1:
The system segments virtual elements into two groups: those requiring physics engine modeling and those using pre-generated animation data. This segmentation reduces the computational workload on the physics engine during rendering, allowing higher frame rates while maintaining physical interaction accuracy for elements where it is essential.
Solution Approach 2:
Animation data is generated in advance during an offline training phase using the physics engine, then stored for efficient retrieval during gameplay. This preliminary computation reduces real-time physics engine workload, enabling higher frame rates while preserving physical interaction accuracy through the pre-computed animation data.
Data Source
AI summary
A system for animating a first virtual element within a virtual environment, comprising: receiving circuitry configured to receive first state data descriptive of a state of the first virtual element, the first state data comprising one or more kinematic properties of the first virtual element; generating circuitry comprising a generating model trained to generate, based on the received first state data, animation data to be applied to the first virtual element; and animating circuitry configured to apply the generated animation data to the first virtual element.