3D Character Generation Using Priority Blend Shape Sequences
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Solution Overview
Problem
Automatically generating three-dimensional computer-generated characters using random numbers for blend weights often results in discontinuities, folds, or abrupt changes in the mesh, leading to grotesque character combinations, and requires high processing power and memory, with no guarantee of visually pleasing results.
Innovation Solution
A method of generating a sequence of sets of blend shapes in order of increasing priority, with randomization and blending techniques applied to a mesh upfront, reducing runtime computation and ensuring smooth transitions, while storing data compactly and allowing for easy updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If random numbers are used to set blend weights for automatic character generation, then automation is achieved, but the result contains discontinuities, folds, or abrupt changes in the mesh
Solution Approach 1:
The patent changes the parameters by organizing blend shapes into priority sequences and using weighted combinations instead of pure random selection. This transforms the randomization approach from direct blend weight assignment to a structured parameter selection process that maintains mesh continuity while achieving automation.
Solution Approach 2:
The patent applies preliminary action by pre-establishing the blend shape sequence and weight parameters before actual character generation. This preparation ensures that when characters are automatically generated, the mesh transitions remain smooth because the compatibility relationships between blend shapes have been predetermined.
2Adaptability or versatility
If multiple blend shapes are applied to generate diverse characters, then character diversity increases, but processing power and memory requirements increase
Solution Approach 1:
The patent segments the blend shapes into ordered sequences with different priorities. This segmentation allows the system to manage diverse blend shapes in an organized manner, processing them in controlled stages rather than all at once, thereby reducing peak processing requirements while maintaining character diversity.
Solution Approach 2:
The system uses self-service through the predetermined blend shape sequences that automatically guide the character generation process. Once the sequences are established, the automatic generation process follows these pre-defined paths, reducing the need for complex real-time processing decisions and lowering computational overhead.
3Adaptability or versatility
If multiple blend shapes are applied to generate diverse characters, then character diversity increases, but memory usage increases
Solution Approach 1:
By segmenting blend shapes into priority-based sequences, the patent enables efficient memory management. The system stores blend shape data in an organized hierarchical structure rather than as unstructured data, allowing for compact representation and reducing overall memory requirements while supporting diverse character generation.
4Manufacturing precision
If blend shapes are applied manually by artists, then visual quality is ensured, but productivity decreases
Solution Approach 1:
The patent changes the approach from manual artistic judgment to automated parameter-based selection. By defining blend shapes with priority levels and weight parameters, the system automatically reproduces visually pleasing results without requiring artist intervention for each character, thereby maintaining quality while increasing productivity.
Solution Approach 2:
The patent creates reusable blend shape sequences that can be copied and applied to generate multiple characters consistently. Once a visually pleasing combination is established through the parameter system, it can be replicated automatically, maintaining the quality standard across many characters without requiring repeated manual adjustment.
Data Source
AI summary
A method of automatically generating a three-dimensional entity is described. A sequence is generated comprising sets of blend shapes in order of increasing priority. Each set of blend shapes comprises one or more blend shape identifiers and parameters defining candidate blend weights for each blend shape. For each of the sets of blend shapes in the sequence and in order of increasing priority: the one or more blend shape identifiers in the set are added to a set of blend shapes for the entity. Dependent upon whether blend shape identifier that is added is already present in that set, the parameters for the blend shape are either added or updated. One or more blend shapes from the set of blend shapes for the entity are then added to a mesh of the entity using a randomization method and the resultant mesh is stored in memory.