3D Object Animation Rig Transfer Using a Universal ML Model
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Solution Overview
Problem
Existing systems require time-consuming and resource-intensive processes to generate animation data for 3D objects, particularly when accounting for different types of objects, which is inefficient and burdensome on computing resources.
Innovation Solution
A machine-learning model trained using specialized animation rigs is employed to generate animation data for 3D objects, capable of covering various types of objects, reducing the need for separate models and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional animation generation methods are used for different types of 3D objects, then animation data can be generated for each specific object type, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent applies universality by training a single machine learning model on a diverse dataset containing multiple object types (humans, animals, vehicles, etc.), enabling the model to generate animation data for various 3D objects without requiring separate models for each object type. This universal approach reduces the time and resources needed compared to traditional methods that would require creating and training separate animation systems for each object category.
2Measurement precision
If separate machine learning models are created for each object type, then animation accuracy is improved, but computing resources and model complexity increase
Solution Approach 1:
The patent employs a single universal machine learning model that can handle multiple object types, thereby reducing model complexity and computational resource requirements. The model achieves adequate animation accuracy across different object types by learning common animation patterns and principles from a diverse training dataset, eliminating the need to create and maintain multiple specialized models.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the input representation and conditioning parameters of the universal model based on the specific object type being animated. Rather than creating separate models, the system modifies input parameters and conditioning information to adapt the single model's behavior to different object types, maintaining animation accuracy while reducing complexity.
3Reliability
If comprehensive animation rigs covering all features are used, then animation quality is improved, but the data size and processing requirements increase
Solution Approach 1:
The patent extracts and focuses on the essential animation features and parameters needed for generating quality animations, rather than processing all possible features. The machine learning model learns to identify and process only the critical motion patterns and key parameters from the training data, reducing the effective data size and processing requirements while maintaining animation quality.
Data Source
AI summary
A method includes identifying a first three-dimensional (3D) object comprising a first animation rig covering a first set of features associated with the first 3D object. An indication of the first 3D object is provided as input to a machine-learning model. The machine-learning model is trained to generate, using a second 3D object comprising a second animation rig, an animation sequence for the first 3D object. The second animation rig covers a second set of features associated with the second 3D object and the second set of features comprises the first set of features and one or more additional features. One or more outputs of the machine-learning model is obtained. The one or more obtained outputs comprise a plurality of animation frames reflecting an animation sequence for the first 3D object.