3D Character Rigging via Automated Mesh Segmentation
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Solution Overview
Problem
The creation of animated 3D characters requires specialized training and manual effort, particularly in creating rigged meshes, which is a time-consuming and labor-intensive process involving the identification of key points and bone sets for character movement and skinning weights.
Innovation Solution
Automated systems and methods for rigging a 3D character's facial mesh using machine learning processes, such as JointBoost and Conditional Random Fields, to generate a representative mesh, determine segments and key points, and calculate skinning weights, which are then translated into the original mesh to create a rigged mesh.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual forward kinematic rigging is used to create skeleton and skinning weights, then animation quality and precision are improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical rigging operations with an automated computer-based system that uses machine learning and image processing to generate skeleton structures and skinning weights automatically, eliminating the need for manual manipulation while maintaining rigging quality
Solution Approach 2:
The system enables the mesh model to rig itself automatically through self-organizing algorithms that identify key points, generate bone structures, and calculate skinning weights without human intervention, allowing the model to perform its own preparation for animation
2Reliability
If specialized 3D animation artists perform rigging manually, then animation quality is improved, but device complexity and training requirements worsen
Solution Approach 1:
The patent replaces the need for specialized human artists with an automated computer-based rigging system that uses machine learning algorithms to perform tasks previously requiring expert knowledge, thereby eliminating training requirements while maintaining animation quality
Solution Approach 2:
The system learns from training data containing examples of properly rigged models and replicates the rigging process automatically, copying successful rigging patterns from the training set to new models without requiring human expertise
3Measurement precision
If manual identification of key points and bone sets is performed, then segmentation accuracy is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary training on a dataset of labeled examples before actual use, learning to identify key points and segments automatically. This preliminary learning phase enables rapid and accurate identification during deployment without manual intervention
Solution Approach 2:
The patent replaces manual visual inspection and identification of key points with automated image processing and machine learning algorithms that analyze mesh geometry and automatically locate important features with high precision
Data Source
AI summary
A system and method for automatic rigging of three dimensional characters for facial animation provide a rigged mesh for an original three dimensional mesh. A representative mesh is generated from the original mesh. Segments, key points, a bone set, and skinning weights are then determined for the representative mesh. The Skinning weights and bone set are placed in the original mesh to generate the rigged mesh.