Animation Graph Generation for New Motion State Transitions
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Solution Overview
Problem
Motion output systems for virtual agents lack the ability to transition between states not included in their pre-defined animation libraries, limiting the versatility and realism of virtual agent movements.
Innovation Solution
Generate an animation graph by connecting nodes for pre-existing animations and create transitional animations using a graph neural network to extend the motion graph, allowing for new transitions between states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a library of hand-crafted animations is used for a virtual agent, then the animation quality and control are improved, but the system cannot provide transitions between states not included in the library
Solution Approach 1:
The system pre-processes the animation library to extract motion states, transitions, and metadata, building a structured animation graph in advance. This preliminary organization enables the system to efficiently query and generate transitions during runtime without requiring all transitions to be manually pre-defined.
Solution Approach 2:
The patent introduces an animation graph as an intermediary data structure between the hand-crafted animation library and the motion output system. The graph neural network serves as another intermediary that learns to generate new transitions by analyzing the structure and content of the animation graph, bridging the gap between limited hand-crafted animations and the need for versatile transitions.
2Adaptability or versatility
If all possible transitions are included in the animation library, then the versatility of motion states is improved, but the complexity and time required to create the library increases
Solution Approach 1:
The animation graph structure and graph neural network model serve multiple functions: they organize existing animations, enable querying of available transitions, and generate new transitions that were not explicitly defined in the original library. This multi-functionality allows the system to handle both simple and complex transition scenarios with a unified approach.
Solution Approach 2:
The system enables self-service by allowing the animation graph and graph neural network to automatically generate transitions between motion states without requiring manual creation of every possible transition. The model learns from the provided animation library and autonomously creates new transition paths, reducing the burden of manually populating the entire transition space.
3Manufacturing precision
If manual creation of all transitions is performed, then the precision of motion transitions is improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary processing of the animation library to extract structured information about motion states, transitions, and metadata. This pre-extraction enables the graph neural network to learn from the organized data and generate precise transitions without requiring manual specification of each transition's details.
Solution Approach 2:
The patent replaces the manual mechanical process of creating and organizing animation transitions with an automated learning-based system. The graph neural network automatically analyzes the animation graph structure and generates transitions, substituting the time-consuming manual workflow with an intelligent automated process that maintains precision while dramatically reducing time investment.
Data Source
AI summary
In some implementations, the method includes: obtaining a plurality of animations; determining initial and end motion states for each of the plurality of animations; generating an animation graph including nodes for each of the plurality of animations by connecting, with a directional edge, a first node with an end motion state to a second node with an initial motion state that matches the end motion state of the first node; generating a transitional animation that is not included among the plurality of animations from an initial reference motion state to a target motion state that corresponds to a path that traverses the animation graph from a third node associated with the initial reference motion state to a fourth node associated with the target motion state; and updating the animation graph by removing one or more nodes from the animation graph based at least in part on the transitional animation.