Point Cloud Rigging System for Animation
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Solution Overview
Problem
Animation of point clouds is challenging due to the lack of a wireframe structure, requiring painstaking connection of millions of data points to a skeletal framework, which is time-consuming and difficult, especially when dealing with non-uniformly distributed data points in 3D space.
Innovation Solution
The Point Cloud Rigging System (PCRS) employs AI/ML techniques for semi-autonomous or fully autonomous linking of point cloud data points to a skeletal framework, defining weights and animation, allowing for concerted movements without a digital wireframe, using interactive tools and refining selections based on positional and non-positional elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual rigging methods are used to connect point cloud data points to a skeletal framework, then animation capability is achieved, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service automation where the rigging process automatically connects point cloud data to skeletal frameworks without requiring manual intervention for each connection. The AI/ML algorithms independently perform the complex task of mapping millions of data points to bones, eliminating the need for painstaking manual rigging while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of connecting point clouds to skeletons with an automated computational system using AI/ML algorithms. This substitution transforms the labor-intensive manual rigging process into an automated digital workflow that can handle complex point cloud data efficiently.
2Ease of operation
If manual rigging of millions of data points is performed, then complete control over animation is achieved, but the complexity and difficulty of the process increases significantly
Solution Approach 1:
The system introduces an intermediary AI/ML layer that acts as a mediator between the point cloud data and the skeletal framework. This intermediary automatically performs the complex mapping and weighting calculations, simplifying the user interface while handling the computational complexity behind the scenes.
Solution Approach 2:
The patent extracts the complex computational tasks of point cloud analysis, bone mapping, and weight calculation from the manual operation process. By separating these complex functions into automated AI/ML components, the system reduces the operational complexity users must directly manage while maintaining complete control over the animation process.
3Extent of automation
If traditional rigging methods are used without AI/ML assistance, then simplicity of implementation is maintained, but automation and time reduction are limited
Solution Approach 1:
The system performs preliminary actions by pre-processing point cloud data and pre-establishing mappings to skeletal frameworks before animation begins. The AI/ML algorithms automatically analyze the point cloud structure, identify relevant features, and create initial weight distributions, preparing the data in advance to enable rapid automation during the actual rigging process.
Solution Approach 2:
The patent uses copying by creating virtual representations and models of the point cloud data and skeletal frameworks. The AI/ML system works with these digital copies and models to perform automated mapping and weighting, allowing the system to achieve high automation levels by operating on replicated data structures rather than requiring complex real-time processing of original data.
Data Source
AI summary
Disclosed is a rigging system for animating the detached and non-uniformly distributed data points of a point cloud. In response to a selection of a region of space in which a first set of data points are located, the system may identify commonality in the positional or non-positional elements of a first subset of the first set of data points, and may determine that a second subset of the first set of data points lack the commonality. The system may refine the first set of data points to a second set of data points that includes the first subset of data points and that excludes the second subset of data points. The system may link the second set of data points to a bone of a skeletal framework, and may animate the second set of data points based on an animation that is defined for the bone.