3D Secondary Motion Modeling via ML Descriptors
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Solution Overview
Problem
Conventional techniques for modeling secondary motion rely on two-dimensional models, which are prone to depth ambiguity and require a prohibitive amount of training data, leading to overfitting, especially when limited data is used.
Innovation Solution
The implementation of a secondary motion modeling system that uses three-dimensional object models and motion descriptors to generate plausible secondary motion, reducing the need for extensive training data by leveraging machine learning pose, encoder, shape decoder, and appearance decoder models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional two-dimensional models are used to model secondary motion, then the system can process simpler data, but depth ambiguity arises and a prohibitive amount of training data is required
Solution Approach 1:
The patent transitions from two-dimensional motion models to three-dimensional motion models. The system represents motion in 3D space using three-dimensional motion descriptors that capture surface normals and velocities in three dimensions, eliminating depth ambiguity inherent in 2D representations. This dimensional upgrade allows the system to accurately represent out-of-plane body motion without requiring prohibitive amounts of training data.
2Quantity of substance
If conventional two-dimensional models are used with limited training data, then training resources are reduced, but overfitting occurs in synthesized secondary motion
Solution Approach 1:
The patent changes the parameter space from two-dimensional to three-dimensional motion descriptors. By representing motion in 3D with surface normals and three-dimensional velocities, the system creates a more robust parameterization that generalizes better from limited training data. The three-dimensional representation captures essential motion dynamics that are invariant to viewpoint changes, reducing overfitting when training data is limited.
3Measurement precision
If three-dimensional models are used to represent motion, then depth ambiguity is eliminated and out-of-plane motion accuracy improves, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical multi-camera systems with a single-camera setup that uses three-dimensional motion descriptors. Instead of physically capturing motion from multiple viewpoints, the system uses machine learning models (encoder, shape decoder, appearance decoder) to infer and represent three-dimensional motion from single-viewpoint video data, achieving 3D motion representation without the mechanical complexity of multiple cameras.
4Loss of information
If conventional techniques are used to capture motion from multiple cameras and viewpoints, then comprehensive motion data is obtained, but data collection complexity and cost increase
Solution Approach 1:
The patent uses a single-camera system that captures video data, then employs machine learning models to create three-dimensional motion descriptors that effectively 'copy' or reconstruct the information that would be obtained from multiple cameras. The encoder model processes single-viewpoint video and generates three-dimensional representations that simulate multi-viewpoint data, eliminating the need for physically deploying multiple cameras while retaining motion information completeness.
Data Source
AI summary
Techniques for modeling secondary motion based on three-dimensional models are described as implemented by a secondary motion modeling system, which is configured to receive a plurality of three-dimensional object models representing an object. Based on the three-dimensional object models, the secondary motion modeling system determines three-dimensional motion descriptors of a particular three-dimensional object model using one or more machine learning models. Based on the three-dimensional motion descriptors, the secondary motion modeling system models at least one feature subjected to secondary motion using the one or more machine learning models. The particular three-dimensional object model having the at least one feature is rendered by the secondary motion modeling system.