Continuous Curve Texture Synthesis via Graph Topology
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Solution Overview
Problem
Existing methods for generating continuous curve textures are limited, as they primarily focus on discrete patterns and do not effectively handle continuous curves, which require significant manual effort and expertise, and lack automation for complex patterns.
Innovation Solution
An intelligent authoring system that generates continuous curve textures by capturing the geometry and topology of an input exemplar, using robust neighborhood matching and assignment techniques to synthesize and optimize the output, allowing for automatic creation and interactive editing of patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to create repetitive patterns, then high degrees of individual freedom and customization are achieved, but significant technical expertise and manual labor are required
Solution Approach 1:
The system captures local neighborhood patterns from user-drawn exemplars and automatically copies them to generate the complete pattern. The user only needs to draw a small representative portion, and the system replicates it across the entire output area, significantly reducing manual labor while preserving design freedom
Solution Approach 2:
The system performs preliminary analysis of the user-drawn exemplar to extract geometric and topological features before generating the complete pattern. This preliminary processing enables automatic synthesis while maintaining the designer's intent, reducing the need for extensive manual drawing
2Productivity
If automatic methods are used to synthesize patterns, then manual labor is reduced, but existing techniques mainly focus on discrete patterns and do not apply to general continuous curves
Solution Approach 1:
The system uses a unified graph-based representation that can model both discrete patterns and continuous curves within the same framework. This multi-functional approach allows automatic synthesis to work across different pattern types, including connected and intersecting curves, without requiring separate specialized techniques
Solution Approach 2:
The system transitions from discrete pixel-based parameters to continuous curve parameters by representing patterns as graphs with geometric and topological attributes. This parameter transformation enables automatic synthesis methods to handle continuous curves while maintaining the computational efficiency needed for automation
3Loss of time
If continuous curve textures are generated automatically, then time consumption is reduced, but ensuring similarity between input exemplar and output texture requires sophisticated matching techniques
Solution Approach 1:
The system segments the pattern synthesis problem into independent local neighborhood matching tasks. By comparing and replicating small local regions rather than attempting to match entire complex patterns, the system reduces the computational complexity of the matching algorithm while still ensuring overall similarity through iterative optimization
Solution Approach 2:
The system employs iterative optimization with feedback mechanisms that progressively refine the output pattern to better match the input exemplar. Each iteration uses the previous result to guide further adjustments, allowing sophisticated similarity matching to be achieved through multiple simpler steps rather than a single complex operation
Data Source
AI summary
Embodiments are disclosed for generating continuous curve textures based on an input exemplar. A method of generating continuous curve textures may include receiving an input exemplar which represents a repetitive pattern as a plurality of vector curves, generating an input graph representation of the input exemplar which represents a geometry and a topology of the input exemplar, synthesizing an output graph based on the input graph representation, and reconstructing output vector curves from the output graph.