Generative Facial Models for Granular Character Animation
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Solution Overview
Problem
Existing methods for generating facial models and animations for virtual characters in electronic games are labor-intensive and lack transferability and granularity, requiring manual adjustment of each character's face to achieve realistic expressions, which are not easily adjustable for variations.
Innovation Solution
A computer-implemented method utilizing machine learning techniques, including generative models, to automate the generation and optimization of facial models and animations by accessing trained machine learning models to create 3D meshes and 2D texture maps, and employing a differentiable rendering engine to refine these models based on target photorealistic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual methods are used to create facial models and expressions for each character, then each character can have unique detailed expressions, but the process requires substantial time and labor from game designers
Solution Approach 1:
The system uses generative models to learn representations of human faces from training data and automatically generates facial models and expressions by copying learned patterns, eliminating the need for manual creation of each character's facial features and expressions
Solution Approach 2:
The patent replaces the manual mechanical process of modeling facial expressions with machine learning-based automated generation, where algorithms substitute human designers in creating and adjusting facial models
2Adaptability or versatility
If manual methods are used to create facial expressions, then expressions can be customized for each character, but the expressions lack granularity and cannot easily represent variations in emotion
Solution Approach 1:
The system generates dynamic and continuous facial expressions by sampling from learned latent spaces, allowing for smooth transitions and infinite variations between emotions rather than discrete pre-defined expressions
Solution Approach 2:
The patent uses parameter-based control of facial expressions through learned representations, where continuous parameter adjustments in the latent space enable fine-grained control over expression variations and emotional nuances
3Reliability
If detailed high-resolution facial models are created for realistic gameplay, then character realism is improved, but the computational burden and complexity increase substantially
Solution Approach 1:
The system segments facial modeling into separate learnable components such as identity, expression, and pose representations, allowing complex realistic faces to be constructed from simpler modular elements that are independently generated and combined
Data Source
AI summary
Systems and methods are provided for enhanced animation generation based on generative modeling. An example method includes training models based on faces and information associated with persons. The modeling system being trained to reconstruct expressions, textures, and models of persons.