Motion Prediction and Refinement for Dynamic Object Interaction
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Solution Overview
Problem
Current computer graphics technologies face challenges in generating and simulating realistic motion of objects, particularly in controlling one object based on the motion of another, especially in dynamic interactions like soccer, where precise control of a character's motion and a ball's trajectory is required.
Innovation Solution
A method and apparatus that predict and refine the motion of a first object based on a motion specification and the trajectory of a second object, generating a vector to control the joints of the first object, and simulating their motion using a dynamic system with feedback loops to ensure accurate and stable control, considering cost functions and constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a motion of a first object is predicted based on a motion specification and simulated in a previous iteration, then the motion can be refined based on the predicted motion and trajectory of a second object, but the computational complexity and processing time increase due to iterative refinement and simulation
Solution Approach 1:
The system performs preliminary motion prediction based on motion specification before iterative refinement, establishing an initial motion trajectory that guides subsequent refinement steps. This preliminary action reduces the computational burden of iterative refinement by providing a informed starting point rather than beginning from scratch in each iteration.
Solution Approach 2:
The system uses feedback from the simulated trajectory of the second object to refine the predicted motion of the first object. The refinement process incorporates information about actual interaction outcomes to adjust and improve motion prediction accuracy in subsequent iterations, creating a closed-loop control system that balances precision with computational efficiency.
2Manufacturing precision
If the motion of the first object is refined based on the predicted motion and trajectory of the second object, then the control accuracy improves, but the device complexity increases due to multiple processing stages
Solution Approach 1:
The motion control system is segmented into distinct functional modules: a motion prediction module that generates initial trajectories, a refinement module that adjusts motion based on interaction feedback, and a simulation module that computes trajectories of controlled objects. This segmentation allows each module to specialize in specific tasks, improving overall precision while managing complexity through modular design.
Solution Approach 2:
The system introduces an intermediary refinement process between motion prediction and final execution. This intermediary module processes the predicted motion and trajectory information, applying corrections based on interaction physics and constraints before generating final control commands. This mediator layer improves control precision by decoupling the complexity of direct control from the prediction process.
3Reliability
If vectors are generated to control joints of the first object based on refined motion, then the motion naturalness improves, but the computational load increases due to joint-level control calculations
Solution Approach 1:
The system applies local quality control by generating vectors specifically for controlling joints of the first object based on refined motion data. Rather than applying uniform control across all degrees of freedom, the system focuses computational resources on critical joints that most significantly impact motion naturalness, such as limb joints during interaction. This localized approach improves motion realism while reducing overall computational energy consumption.
4Measurement precision
If iterative motion prediction and refinement is performed, then the simulation accuracy improves, but the productivity decreases due to multiple iterations required
Solution Approach 1:
The system performs preliminary motion prediction using motion specification parameters before entering iterative refinement. This preliminary action establishes a high-quality initial guess that is already close to the final solution, significantly reducing the number of iterations required to achieve convergence. As a result, simulation accuracy is maintained while productivity is improved by minimizing redundant iterative computations.
Data Source
AI summary
A method of simulating a motion of an object controlling another object based on an example motion or a previously generated motion of the object is disclosed. The method includes refining a motion of a first object based on a motion of the first object predicted based on a motion specification, and on a trajectory of a second object controlled by the first object, and simulating the motion of the first object and the trajectory of the second object based on a vector for controlling joints of the first object that is generated based on the refined motion.