Point Multiplication Animation with Soft Caching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer animation techniques, such as kinematic articulation and principal component analysis, face challenges in efficiently handling nonlinear relationships between animation variables and outputs, leading to large training sets and unbounded errors, especially when dealing with novel poses or lighting configurations.
Innovation Solution
The Point Multiplication process identifies key points in graphical calculations, using a statistical subspace model to estimate full geometric calculations, with importance sampling for global illumination, and a soft caching mechanism to manage errors, allowing for accelerated posing and rendering processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If kinematic articulation or principal component analysis is used to represent geometric objects and illumination, then the animation process can be simplified, but the training sets required become very large and errors become unbounded for novel poses
Solution Approach 1:
The patent segments the animation process into two distinct phases: an offline training phase where a statistical model is built from a training set of poses, and an online animation phase where the model is applied to generate new poses. This segmentation allows the complex task of handling all possible poses to be divided into model construction (done once) and model application (done repeatedly), resolving the contradiction by preparing reliability in advance while maintaining simplicity during actual animation.
Solution Approach 2:
The patent performs preliminary action by constructing a statistical model offline from a training set before actual animation occurs. This preliminary model construction captures the relationships between animation variables and geometric/illumination properties in advance, so that during online animation, these pre-computed relationships can be applied directly without requiring large training sets during the animation process itself, thereby ensuring reliability while maintaining simplicity.
2Loss of time
If statistical models are used to represent geometric objects and illumination, then computation time is reduced, but large training sets are required and errors are unbounded for novel configurations
Solution Approach 1:
The patent performs the computationally intensive work of building the statistical model in advance, during an offline training phase. This preliminary action computes and stores the relationships between animation variables and geometric/illumination properties before actual animation occurs. During online animation, these pre-computed models are applied directly, dramatically reducing computation time while requiring the training set to be prepared only once, not for each animation frame.
Solution Approach 2:
The patent segments the computational workload into offline model construction (computationally intensive, done once) and online model application (computationally efficient, done repeatedly). This segmentation resolves the contradiction by concentrating the large training set requirement and heavy computation into the offline phase, while the online phase achieves fast animation with minimal computation and no additional training set requirements.
3Productivity
If point multiplication is used to accelerate animation calculations, then computational cost is reduced and processing speed increases, but accuracy may be compromised without proper error management
Solution Approach 1:
The patent implements feedback by computing a small subset of points exactly (using full geometric calculations) and comparing these results against the predictions from the accelerated point multiplication method. This feedback mechanism detects where the approximation breaks down, allowing the system to maintain accuracy for critical points while using the faster method for other points, thus resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent applies local quality by treating different points differently: some points are calculated exactly using full geometric methods, while other points use the accelerated statistical model approximation. This localized approach ensures that critical points maintain high accuracy while non-critical points benefit from the speed of the approximate method, resolving the contradiction by allowing different accuracy levels in different locations.
Data Source
AI summary
A method for a computer system includes determining an animation response subspace within an animation response space for an animation variable responsive to training data for the animation variable, determining calculation key points for navigating within the animation response subspace, calculating animation response values for the calculation key points in response to input data for the animation variable, predicting animation response values for points within the animation response space in response to animation response values for calculation key points, determining a difference between a predicted animation response value and a calculated animation response value for a point in the points and in the calculation key points, and when the difference exceeds a first difference, combining the predicted animation response value and the calculated animation response value to form the animation response value for the point.