Avatar Head Mesh Deformation for Automated Rig Skinning
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Solution Overview
Problem
Existing methods for creating avatar heads in virtual experiences require manual skinning of rigs, which is time-consuming and labor-intensive, and often lack realism due to dependence on mesh connectivity.
Innovation Solution
A method involving a conditional diffusion network and global encoder to predict mesh deformations for different poses, followed by skinning and caging processes to automate the rig creation, ensuring accurate and realistic avatar head animations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual skinning of rigs is used to create avatar heads, then the process allows for detailed control and customization, but it is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of skinning rigs with an automated machine learning system. The neural network automatically predicts mesh deformations and generates skinning weights without requiring manual intervention, thereby substituting the mechanical manual operation with an automated computational system that resolves the contradiction between ease of manufacture and time consumption
Solution Approach 2:
The system enables self-service automation where the avatar head creation process performs its own skinning operation through the neural network. The model automatically processes mesh data, predicts deformations, and generates rig skinning results without external manual input, allowing the system to serve itself and eliminate the time-consuming manual skinning step
2Reliability
If manual skinning methods are used, then the process is flexible and adaptable to different designs, but the results often lack realism due to dependence on mesh connectivity
Solution Approach 1:
The patent replaces the manual skinning method that depends on mesh connectivity with a neural network-based system. The machine learning model learns deformation patterns from training data and predicts mesh deformations directly, eliminating the dependency on complex mesh connectivity analysis and thereby improving realism while reducing the complexity associated with manual mesh processing
Solution Approach 2:
The system changes the parameters used for skinning from manual mesh connectivity-based calculations to neural network predicted deformation fields. By transforming the input parameters from geometric mesh data to learned deformation patterns, the system achieves more realistic results without being constrained by the complexities of manual mesh connectivity requirements
3Productivity
If automated methods using neural networks are used, then the process is faster and more efficient, but it requires complex computational models and training data
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on large datasets of mesh deformations and pose variations before deployment. This preliminary training phase prepares the model to quickly process new avatar heads without requiring complex real-time computations, thereby achieving high productivity while managing system complexity through advance preparation
Solution Approach 2:
The system introduces a conditional diffusion network as an intermediary between the input mesh data and the final skinning results. This intermediary model acts as a bridge that simplifies the computational process by breaking it down into manageable steps: global feature extraction, conditional diffusion processing, and deformation prediction, thereby achieving efficiency while managing complexity through modular architecture
Data Source
AI summary
According to one aspect of the present disclosure, a method of mesh-deformation prediction is provided. The method may include generating a set of global features based on mesh information associated with a mesh of an avatar head. The mesh information may include a plurality of first vertex positions of the mesh corresponding to the avatar head being in a neutral pose. The method may include generating a set of mesh deformations for another pose of the avatar head based on the mesh information, the set of global features, and a pose vector associated with the another pose. The set of mesh deformations may be associated with a plurality of second vertex positions of the mesh corresponding to the avatar head in the another pose. The method may include performing a skinning process to generate a plurality of joints and joint weights based on the set of mesh deformations.