Neural Network Vertex Animation for Latency Optimization
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Solution Overview
Problem
Existing techniques for creating realistic and expressive facial animation in computer graphics are either labor-intensive and limited by predefined blendshapes or complex and computationally expensive in terms of direct vertex manipulation, while also facing challenges in real-time efficiency and compatibility.
Innovation Solution
A neural network driven vertex animation method that uses an encoder-decoder architecture to predict vertex positions and generate blendshapes, improving latency and compatibility, and minimizing data transfer by transmitting blendshape coefficients rather than full vertex data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If blendshape animation is used with a predefined set of 3D shapes, then the animation process becomes simpler and more standardized, but the ability to capture the full spectrum of human expression is limited and manual labor is required to create blendshapes
Solution Approach 1:
The system uses neural networks to automatically generate blendshapes and predict vertex positions without requiring manual animation creation. The AI model processes input images and directly outputs animation data, eliminating the need for artists to manually create blendshapes while capturing realistic human expressions
Solution Approach 2:
The patent replaces manual mechanical animation processes with neural network-based automated generation. Instead of artists manually manipulating vertices or creating blendshapes, the system uses deep learning models to automatically generate animation data from input images, substituting human labor with AI computation
2Manufacturing precision
If direct vertex manipulation is used to control individual vertices, then finer control over facial movements is achieved, but the computational cost increases and complexity increases requiring specialized tools and expertise
Solution Approach 1:
The system extracts only the essential animation data needed for rendering by predicting vertex positions directly from input images using neural networks. Instead of requiring complex vertex manipulation tools and expertise, the AI model generates the necessary vertex position data automatically, simplifying the overall system while maintaining fine control over facial movements
Solution Approach 2:
The neural network acts as an intermediary between input images and final animation output. Rather than requiring direct manual vertex manipulation, the AI model serves as a mediator that automatically translates images into vertex position data, reducing system complexity while preserving control precision
3Manufacturing precision
If complete 3D mesh data is transmitted for each frame of animation, then high-fidelity facial animation is achieved, but significant latency and bandwidth consumption occur impairing fluidity and responsiveness
Solution Approach 1:
The system extracts and transmits only the essential animation parameters (vertex positions predicted by neural networks) rather than complete 3D mesh data for each frame. This selective extraction maintains animation fidelity while dramatically reducing data transmission requirements, thereby lowering latency and bandwidth consumption
Solution Approach 2:
The patent changes the data representation from complete 3D mesh geometry to condensed neural network predicted vertex positions. By transforming the animation data into a more efficient parameter format generated by AI, the system maintains visual fidelity while reducing data size and transmission time, improving real-time performance
Data Source
AI summary
A method for neural network driven vertex animation can include receiving, by an encoder component of a trained neural network, an input driving signal including audio data, processing, by the encoder component, the input driving signal to generate blendshape coefficient information based on the input driving signal, transmitting, by the encoder component, the blendshape coefficient information to a decoder component of the trained neural network, receiving, by the decoder component, the blendshape coefficient information from the encoder component, and generating, by the decoder component, vertex position information based on the blendshape coefficient information for animating a 3D model.