Multi-Stroke Neural Network Rendering for Stylized Digital Images
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Solution Overview
Problem
Conventional image stylizing systems face inefficiencies, requiring numerous strokes and significant computing resources, and often trade off between accuracy and flexibility in reproducing digital images with different rendering styles.
Innovation Solution
A multi-stroke neural network is employed to encode digital images, generate stroke parameters, and render them sequentially, utilizing a combination of loss functions to balance efficiency, accuracy, and flexibility in stylization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image stylizing systems use numerous drawing strokes to reproduce digital images, then accuracy of image reproduction is improved, but computing resources and processing time increase significantly
Solution Approach 1:
The patent segments the image reproduction task into multiple drawing strokes with different characteristics (foreground strokes, background strokes, midground strokes). Each stroke type is optimized for specific purposes, allowing accurate reproduction while reducing the total number of strokes needed compared to conventional uniform stroke approaches.
Solution Approach 2:
The system changes parameters of the drawing strokes dynamically, including stroke width, opacity, color, and position, based on the encoded image features. This allows a smaller number of strokes to convey more information and achieve accurate reproduction without requiring excessive computing resources.
2Manufacturing precision
If conventional systems use multiple passes through neural networks to generate strokes, then stroke quality and image fidelity are improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary encoding of the entire image into a compressed representation before generating any strokes. This single-pass encoding captures essential image features that guide subsequent stroke generation, eliminating the need for multiple iterative passes while maintaining stroke quality.
Solution Approach 2:
The system generates strokes in a continuous sequence from the encoded representation without interrupting for re-encoding or intermediate processing. The stroke generation process flows continuously, converting encoded features into visual output in a single operational pass through the neural network.
3Adaptability or versatility
If conventional image stylizing systems use fixed rendering styles, then system complexity is reduced, but flexibility and adaptability to different styles are limited
Solution Approach 1:
The patent creates a universal stroke generation system that can produce multiple rendering styles through a single neural network architecture. By learning style characteristics during training and using style embeddings, the system achieves multi-functionality, handling various artistic styles without requiring separate specialized systems for each style.
Solution Approach 2:
The system controls rendering style flexibility by changing parameters such as stroke width distribution, opacity values, and color selection based on the desired style. This allows adaptive style transfer while maintaining a unified system architecture, avoiding the need for multiple fixed-style systems.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a multi-stroke neural network for modifying a digital image via a plurality of generated stroke parameters in a single pass of the neural network. Specifically, the disclosed system utilizes an encoder neural network to generate an encoding of a digital image. The disclosed system then utilizes a decoder neural network that generates a sequence of stroke parameters for digital drawing strokes from the encoding in a single pass of the encoder neural network and decoder neural network. Additionally, the disclosed system utilizes a renderer neural network to render the digital drawing strokes on a digital canvas according to the sequence of stroke parameters. In additional embodiments, the disclosed system utilizes a balance of loss functions to learn parameters of the multi-stroke neural network to generate stroke parameters according to various rendering styles.