Stroke Sequencing for Complex Sketch and Image Recreation
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Solution Overview
Problem
Existing methods struggle with accurately recreating intricate details of complex artworks due to challenges in representing and sequencing strokes, especially in complex sketches involving shading and textures, and face high computational demands.
Innovation Solution
A method and system that converts an input image into a sketch by identifying vector curves, organizing strokes into clusters based on proximity, determining a shortest path through these clusters, and generating a paint sequence to recreate the image with RGB strokes, using techniques like CLIP loss and geometry loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural painting techniques employing reinforcement learning are used to generate stroke sequences, then non-photo-realistic image recreation is achieved, but high computational demands and lack of inherent sequence order occur
Solution Approach 1:
The patent segments the stroke generation process into multiple independent streams (geometric stream, textural stream, color stream) that process different aspects of stroke generation separately. This segmentation allows each stream to be optimized independently and reduces the computational complexity of the overall system compared to a monolithic reinforcement learning approach.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the input image to extract geometric features, textural features, and color information before stroke generation. The geometric stream identifies candidate strokes in advance, and the textural stream pre-processes texture information, allowing the final stroke sequencing to be more efficient and computationally lighter.
2Manufacturing precision
If deep reinforcement learning is used for stroke sequence generation, then image recreation is attempted, but computational complexity and lack of inherent sequence order increase
Solution Approach 1:
The patent introduces an intermediary stroke ordering module that receives candidate strokes from multiple streams and orchestrates them into a coherent sequence. This intermediary component simplifies the overall system by providing a clear interface between feature extraction and stroke rendering, avoiding the need for complex reinforcement learning architectures.
Solution Approach 2:
The patent changes parameters by using multiple independent streams with different processing approaches rather than a single complex reinforcement learning model. Each stream operates with its own parameters and optimization criteria, allowing for more controlled and computationally efficient stroke generation with inherent sequence ordering.
3Ease of operation
If ML models are trained on labeled stroke data, then stroke sequence learning is achieved, but the method does not scale for complex sketches involving shading and textures
Solution Approach 1:
The patent segments the complex task of handling shading and textures by creating separate textural and color streams. The textural stream specifically processes texture information and shading patterns, while the color stream handles color information. This segmentation allows the system to scale to complex sketches by independently optimizing each stream for its specific function.
Solution Approach 2:
The patent adds another dimension by introducing multiple parallel processing streams (geometric, textural, color) instead of using a single flat processing approach. This multi-dimensional architecture allows the system to handle complex sketches with shading and textures by processing different aspects of the image in separate dimensions simultaneously.
Data Source
AI summary
Existing sketch generation techniques have disadvantages such as less accuracy over complex sketches, incapable of scaling for complex sketches involving shading and textures, and high computational demands of deep reinforcement learning and lack an inherent sequence order while generating strokes. Embodiments disclosed herein provide a method and system which converts an input image to a sketch, and further obtains an associated sequence of strokes. Further, a sketch sequencing to orchestrate the sequence of strokes is performed, during which a stroke sequence is generated for a constructed sketch of strokes. Based on the generated stroke sequence, the input image is recreated. Further, a paint sequence for the recreated image is generated, wherein by executing the paint sequence, the recreated image is painted to match color of the input image.