Facial Expression Tracking Neural Network for Animation
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Solution Overview
Problem
Current methods for creating animations that mimic a user's facial expressions are time-consuming, labor-intensive, and often result in unnatural and less vivid expressions due to the difficulty in adjusting expression control parameters.
Innovation Solution
An animation generation method using a neural network that drives a role model with expression parameters to generate virtual images, applies real facial images to produce training samples, trains a tracking neural network, and predicts expression parameters to control a second role model, allowing for rich and natural expressions without repetitive parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of expression control parameters is used, then animation expressions can be created, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical adjustment process with an automated neural network system. The tracking neural network automatically predicts expression parameters from input images, substituting the manual parameter adjustment mechanism with an intelligent automated system that processes expressions through learning rather than human intervention.
Solution Approach 2:
The system enables self-service by allowing the neural network to automatically generate expression parameters without human intervention. The tracking neural network serves itself by learning from training data and independently predicting expression parameters for new inputs, eliminating the need for manual parameter tuning.
2Reliability
If manual parameter adjustment is used, then animation expressions can be created, but the expressions are not vivid and natural
Solution Approach 1:
The patent replaces manual parameter adjustment with neural network-based automatic prediction. The tracking neural network learns the complex mappings between facial expressions and control parameters, substituting human judgment and manual tuning with an intelligent system that captures nuanced expression patterns more accurately.
Solution Approach 2:
The system changes from fixed manual parameter setting to dynamic parameter prediction. The neural network generates expression parameters based on learned patterns from training data, allowing parameters to adapt naturally to different expression contexts rather than requiring manual specification for each case.
3Extent of automation
If a tracking neural network is trained and deployed, then expression parameters can be predicted directly, but extensive training data preparation is required
Solution Approach 1:
The patent applies preliminary action by preparing training data and training the neural network in advance. The tedious work of data collection, preprocessing, and model training is performed beforehand, creating a ready-to-use tracking neural network that can then automatically predict parameters without requiring further manual intervention or complex operations during actual use.
Data Source
AI summary
An animation generation method for tracking a facial expression and a neural network training method thereof are provided. The animation generation method for tracking a facial expression includes: driving a first role model according to an expression parameter set to obtain a virtual expression image corresponding to the expression parameter set; applying a plurality of real facial images to the virtual expression image corresponding to the facial expression respectively to generate a plurality of real expression images; training a tracking neural network according to the expression parameter set and the real expression images; inputting a target facial image to the trained tracking neural network to obtain a predicted expression parameter set; and using the predicted expression parameter set to control a second role model.