Automated Facial Expression Motion Generation via Skeleton Parameter Iteration
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Solution Overview
Problem
Existing methods for generating smooth motion in facial expressions of animated characters require manual adjustment of numerous vertices in three-dimensional face models, leading to high workload and cost due to the complexity of face models and the need for multiple groups of skeleton parameters.
Innovation Solution
A method and apparatus for motion image generation that involves obtaining a pre-drawn target face model, selecting a basic face model from a library matched with the target model, determining an initial face model based on skeleton parameters and a skin matrix, and iteratively adjusting these parameters to minimize the error between the initial and target models, thereby generating smooth motion frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of vertices is performed to generate smooth facial expression motion, then the quality of facial expression transformation is improved, but the workload and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical adjustment of vertices with an automated computational system. The system uses skeleton parameters and skin matrices to automatically transform face models, eliminating the need for manual vertex adjustment while maintaining smooth facial expression transitions and reducing workload significantly
Solution Approach 2:
The patent transforms the face model adjustment process from manual vertex manipulation to automated parameter modification. By changing skeleton parameters (bone transformations) and skin matrices (weighting coefficients), the system achieves smooth facial expression changes without manual intervention, thereby improving productivity while maintaining quality
2Adaptability or versatility
If multiple groups of skeleton parameters are generated for different facial expressions, then the versatility of facial expression animation is improved, but the complexity of parameter management increases
Solution Approach 1:
The patent creates a universal face model framework where a single base face model can generate multiple facial expression variations through automated parameter transformation. The skin matrix and skeleton parameters work together as a universal system that can produce diverse expressions without requiring separate manual adjustments for each expression type, thereby reducing parameter management complexity
Solution Approach 2:
The patent pre-establishes the face model, skeleton structure, and skin matrix relationships before animation. By preparing the foundational model and transformation rules in advance, the system can efficiently generate multiple facial expression groups through automated parameter application, reducing the complexity of managing and generating multiple parameter sets
Data Source
AI summary
The present disclosure provides a motion image generation method and apparatus, and a computer device and a storage medium. The method includes: obtaining a pre-drawn target face model; selecting, from a basic face library, at least one basic face model that is matched with the target face model, and determining an initial face model based on skeleton parameters and a skin matrix which respectively correspond to the at least one basic face model; and iteratively adjusting the skeleton parameters of the initial face model based on the initial face model and the target face model to obtain reference skeleton parameters when an error between the initial face model and the target face model is smallest, wherein the reference skeleton parameters are used for producing and generating each frame of images when the target face model moves.