Neural Network Post-Processing for Avatar Facial Expression Fidelity
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Solution Overview
Problem
Existing 3-D rendering methods for avatars result in poor-quality facial expressions due to limited resources, and traditional methods like Blendshapes require manual rebuilding for each person, leading to unrealistic face movements and high computational costs.
Innovation Solution
A system utilizing a neural network (NN) to post-process classically rendered avatars, allowing for high-fidelity facial expressions that preserve individual identity without requiring high computational power or manual rebuilding of Blendshapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional 3-D rendering methods are used for avatars, then computational resources are limited and processing is efficient, but facial expression quality is poor
Solution Approach 1:
The system separates the avatar rendering process into two independent stages: (1) classical 3-D rendering to generate base avatar images, and (2) neural network post-processing to enhance facial expressions. This segmentation allows each stage to optimize for its specific function, with the NN stage focusing solely on expression quality using computational resources efficiently.
Solution Approach 2:
A neural network serves as an intermediary between the classical rendering pipeline and the final output. The NN takes classically rendered avatars as input and produces enhanced versions with high-fidelity facial expressions, acting as a mediator that bridges the gap between computational efficiency and expression quality.
2Ease of operation
If Blendshapes are used for avatar animation, then facial expressions can be generated, but manual rebuilding is required for each person and face movements appear unrealistic
Solution Approach 1:
Instead of manually creating Blendshapes for each person, the system uses a neural network trained on diverse facial data to automatically generate and copy realistic facial expressions. The NN learns from training data and can replicate natural facial movements and expressions for any avatar, eliminating the need for manual reconstruction while maintaining high realism.
3Manufacturing precision
If manual shader crafting is performed for each avatar, then facial expression quality can be improved, but computational power requirements increase and time consumption increases
Solution Approach 1:
The system changes the approach from manually adjusting shader parameters for each avatar to using a pre-trained neural network that automatically adjusts expression parameters. The NN has learned optimal parameter transformations from training data, allowing it to rapidly generate accurate facial expressions without manual shader crafting or high computational power requirements during inference.
Data Source
AI summary
A system and method for providing a facial expression to a virtual avatar. The system includes a training system to train a neural network system to replace a face of the virtual avatar with a source face and to provide a facial expression of the source face to the face of the avatar in a real-time and an inference system configured to use the trained neural network system to provide one or more facial expressions of the source face to the face of the avatar in real-time to cause the one or more facial expressions of the avatar to imitate approximately in an exact manner the one or more facial expressions of a source face media which is represented by the virtual avatar.