Animated Content Repurposing via Latent Space Interpolation
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Solution Overview
Problem
Current methods for synthesizing new animated content from existing computer-generated characters are labor-intensive and struggle with efficiently generating compelling facial animations for non-human characters, as they often rely on full-body motion capture data and lack effective techniques for handling higher dimensional spaces and smaller data sets.
Innovation Solution
The approach involves mapping rig control parameters to a latent space using a Gaussian Process Latent Variable Model (GPLVM) and Principal Component Analysis (PCA) to reduce dimensions, creating a semantic model that allows for the generation of new animated content by interpolating between existing frames, thus enabling the synthesis of new animations without relying on specific rig control implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional statistical methods like GPLVM are used to synthesize new motion data, then dimensionality reduction is achieved, but similar data points are not kept close together in the reduced dimensional space
Solution Approach 1:
The patent introduces an intermediary metric learning component that acts as a mediator between the GPLVM dimensionality reduction and the final animation synthesis. This intermediary learns a custom distance metric in the latent space that ensures similar motion data points remain close together, resolving the contradiction by adding a intermediate processing step that preserves local structures while maintaining dimensionality reduction benefits.
2Reliability
If GPLVM modifications are applied to preserve local distances and model time dependencies, then animation data modeling improves, but the computational complexity and model complexity increase
Solution Approach 1:
The patent segments the animation synthesis process into distinct modular components: GPLVM for dimensionality reduction, metric learning for distance preservation, dynamic models for time dependencies, and connectivity priors for structural integrity. This segmentation allows each component to be optimized independently and combined systematically, improving overall modeling accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent creates a universal animation synthesis framework that can handle multiple types of animation data (full-body motion capture, facial expressions, hand animations) and various data sources (motion capture recordings, manually created animation) through a single integrated system. This multi-functional approach improves reliability across different animation tasks while avoiding the need for separate complex models for each animation type.
3Productivity
If data-driven approaches focus only on full-body tasks with large training sets, then motion graph methods perform well, but smaller data sets lack variety and transitions in motions
Solution Approach 1:
The patent changes the key parameter from requiring large training sets to effectively utilizing small training sets through the introduction of metric learning and improved latent space modeling. By transforming how the system processes and interprets animation data in the latent space, the method achieves high-quality synthesis results even with limited data, making the system adaptable to various data set sizes rather than being constrained to large full-body motion capture databases.
4Ease of manufacture
If existing methods are applied to manually created film-quality animation, then the high dimensional nature of such animation is not adequately handled, as these methods were designed for motion capture data
Solution Approach 1:
The patent applies another dimension approach by systematically addressing the high dimensional nature of film-quality animation through GPLVM-based dimensionality reduction to latent space, followed by metric learning to preserve local structures in this reduced space. This dimensional transformation approach, combined with connectivity priors and dynamic models, enables the system to effectively handle and synthesize high-dimensional manually created animation data that previous methods could not adequately process.
Data Source
AI summary
Systems and methods for automatically animating a character based on an existing corpus of animation are described. The character may be from a previously produced feature animated film, and the data used for training may be the data used to animate the character in the film. A low-dimensional embedding for subsets of the existing animation corresponding to different semantic labels may be learned by mapping high-dimensional rig control parameters to a latent space. A particle model may be used to move within the latent space, thereby generating novel animations corresponding to the space's semantic label, such as a pose. Bridges may link a first pose of a first model within the latent space that is similar to a second pose of a second model of the space. Animations corresponding to transitions between semantic labels may be generated by creating animation paths that traverse a bridge from one model into another.