Animation Model Control Values via Solver Objective Function
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Solution Overview
Problem
Current performance capture systems face challenges in accurately and efficiently transferring a subject's nuanced performance onto a computer-generated representation, particularly in capturing detailed facial expressions and handling noisy data, which results in complex and time-consuming manual corrections.
Innovation Solution
The system determines control values for an animation model using input data from video images, including position and contour information, through a solver that employs an objective function and constraints to match the subject's performance, allowing for intuitive adjustments and robust handling of noisy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correction methods are used to capture nuanced performance, then animation accuracy can be improved, but time consumption and complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical correction processes with an automated solver system that uses objective functions and constraints to compute animation control values. The solver automatically matches performance capture data to animation model controls, eliminating the need for time-consuming manual frame-by-frame adjustment while maintaining high accuracy through mathematical optimization.
Solution Approach 2:
The animation system performs self-correction through the solver, which automatically adjusts control values to minimize error between captured performance and animated output. The system uses built-in objective functions and constraints to self-regulate the animation generation process without requiring external manual intervention, thereby reducing time consumption while preserving accuracy.
2Productivity
If comprehensive constraints are applied to reduce degrees of freedom, then solver efficiency is improved, but animation flexibility may be limited
Solution Approach 1:
The patent implements dynamic constraints that adapt during the solving process. The solver evaluates the animation model's current state and adjusts constraint application accordingly, allowing maximum flexibility where needed while applying constraints only where necessary to improve efficiency. This dynamic approach maintains animation versatility while enhancing solver performance.
Solution Approach 2:
The system applies constraints selectively to specific degrees of freedom rather than uniformly across all controls. The objective function identifies which control parameters require constraint enforcement based on local error patterns, applying restrictions only where they improve efficiency without compromising overall animation flexibility. This localized constraint application preserves adaptability while boosting productivity.
3Adaptability or versatility
If the animation model uses many adjustable controls, then expressiveness is improved, but the complexity of determining control values increases
Solution Approach 1:
The patent replaces the complex manual process of determining control values for numerous adjustable controls with an automated solver system. The solver uses objective functions to systematically evaluate all controls and compute optimal values simultaneously, reducing the complexity from manual sequential adjustment to a single automated computation step while maintaining full expressiveness.
Solution Approach 2:
The solver system serves multiple functions: it handles all adjustable controls uniformly, applies constraints across the entire control space, and optimizes all parameters simultaneously through a single objective function. This universal approach manages the complexity of numerous controls through a unified computational framework rather than separate handling for each control.
4Measurement precision
If the solver considers all degrees of freedom, then animation accuracy is improved, but computational complexity and time increase
Solution Approach 1:
The patent transforms the high-dimensional control space problem into a more manageable form by changing parameters through the objective function formulation. The solver reparameterizes the control values and errors in a way that reduces computational complexity while preserving accuracy, allowing all degrees of freedom to be considered without proportionally increasing computational burden.
Solution Approach 2:
The system replaces exhaustive brute-force evaluation of all degrees of freedom with an optimized solver algorithm that uses gradient-based or iterative methods to converge on accurate control values more efficiently. This computational substitution maintains animation accuracy by considering all controls while reducing overall computational complexity through smart algorithmic approaches.
Data Source
AI summary
Performance capture systems and techniques are provided for capturing a performance of a subject and reproducing an animated performance that tracks the subject's performance. For example, systems and techniques are provided for determining control values for controlling an animation model to define features of a computer-generated representation of a subject based on the performance. A method may include obtaining input data corresponding to a pose performed by the subject, the input data including position information defining positions on a face of the subject. The method may further include obtaining an animation model for the subject that includes adjustable controls that control the animation model to define facial features of the computer-generated representation of the face, and matching one or more of the positions on the face with one or more corresponding positions on the animation model. The matching includes using an objective function to project an error onto a control space of the animation model. The method may further include determining, using the projected error and one or more constraints on the adjustable controls, one or more values for one or more of the adjustable controls. The values are configured to control the animation model to cause the computer-generated representation to perform a representation of the pose using the one or more adjustable controls.