AI Physics Attribution for 3D Point Cloud Animation
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Solution Overview
Problem
Animation of three-dimensional (3D) models lacks realism due to the absence of real-world physics, making it time-consuming and prone to human error to manually define animation physics, especially for complex models, and unintuitive for users unfamiliar with the required parameters.
Innovation Solution
A system using artificial intelligence and machine learning to automatically define and attribute animation physics to 3D models formed by point clouds, analyzing density, distribution, and color properties to classify and simulate realistic movements and interactions, providing intuitive tools for users to visualize and customize physics properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual definition of animation physics is used, then users can control physics parameters, but it is extremely time-consuming and prone to human error
Solution Approach 1:
The system automatically defines animation physics by analyzing the 3D model's geometric properties (surface area, volume, density distribution) and classifying objects into physics categories without requiring manual user input. The AI system serves itself by extracting physical characteristics directly from the model data, eliminating the need for users to manually define physics parameters while maintaining accurate physics simulation.
2Ease of operation
If manual definition of animation physics is used, then users can customize physics properties, but it is unintuitive for users unfamiliar with required parameters
Solution Approach 1:
The system extracts and hides the complexity of physics parameters from the user interface. Instead of presenting users with complex physics equations and parameters, the system automatically derives all necessary physics properties from the 3D model's geometric data, removing the barrier of technical complexity while preserving customization capability through high-level object classification.
3Productivity
If automatic definition of animation physics is used, then time consumption is reduced, but accuracy may be compromised
Solution Approach 1:
The patent replaces manual mechanical definition processes with an AI-based automated system that uses machine learning models trained on physical principles. The system substitutes human expertise with computational analysis that processes 3D model data through neural networks, achieving both high productivity through automation and high reliability through AI-driven accuracy.
4Productivity
If AI-based automatic definition is used, then productivity increases, but system complexity increases
Solution Approach 1:
The AI system is designed as a universal platform that can automatically define physics for any type of 3D model by classifying objects into broad physics categories. The system handles diverse object types (sports equipment, vehicles, characters) through a single unified AI architecture that learns from training data, avoiding the need for separate complex systems for different object types.
Data Source
AI summary
A three-dimensional (3D) animation system automatically assigns accurate animation physics to points of a point cloud to realistically simulate motion of the points in response to different applied forces. The 3D animation system receives the points that are defined with positions in a 3D space and with visual characteristics. The 3D animation system analyzes one or more of the positions and the visual characteristics of the points, classifies the points based on a commonality in the positions or the visual characteristics of the points being associated with a particular classification, and maps a set of animation physics that is defined for the particular classification to the points. The 3D animation system may then animate the points based on the set of animation physics generating an effect in response to a force that is applied to the points.