Non-Rigid Skeleton Model for Human Body Representation
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Solution Overview
Problem
Current human body representation models are inefficient due to their complexity in modeling the diverse skeletal and tissue variations across the population, leading to high computational demands and limited representation of shape variations.
Innovation Solution
A system that uses an articulated tree-structured skeleton model with non-rigid parts to adapt to varying bone lengths and a data-driven shape model decoupled from size characteristics, allowing for more efficient and detailed representation of human body shapes by normalizing skeletal sizes and modeling deformable tissue using a linear subspace.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If a detailed geometric model with many bones and muscles is used to achieve realistic representation, then visual realism is improved, but model complexity and computational requirements increase significantly
Solution Approach 1:
The body model is segmented into two distinct components: a geometric skeletal model for pose and a data-driven shape model for body shape variations. This segmentation allows each component to specialize - the skeleton handles rigid transformations while the shape model handles soft tissue deformations, reducing overall complexity while maintaining realism
Solution Approach 2:
The patent extracts and separates the shape representation from the pose representation. By taking out the shape model as an independent data-driven component decoupled from the geometric skeleton, the system avoids the complexity of modeling all skeletal and muscular details while still achieving realistic body representations
2Device complexity
If a data-driven shape model decoupled from pose is used to simplify the system, then model complexity is reduced, but the ability to represent diverse body shapes and pose variations is limited
Solution Approach 1:
The shape model is designed to be dynamic and adaptable rather than fixed. It uses statistical shape models that can represent variations in body shape across different poses and individuals, allowing the model to adapt to diverse body types while maintaining a computationally efficient structure
Solution Approach 2:
The patent changes the parameters of the shape model to include statistical variations that capture diverse body shapes. By using parameters that represent population-level variations in body geometry, the model can adapt to different individuals and poses without requiring a fully detailed geometric model for each case
3Adaptability or versatility
If bone length variations are included in the geometric skeleton model, then adaptability to individual variations is improved, but the shape model complexity increases
Solution Approach 1:
The patent extracts bone length variations from the geometric skeleton model and represents them in the data-driven shape model instead. This extraction allows the skeleton to remain simple and focused on pose, while the shape model handles individual variations in bone lengths and body proportions through statistical parameters
Data Source
AI summary
For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.