Multi-Stroke Neural Rendering for Single-Pass Image Stylization
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Solution Overview
Problem
Conventional image stylizing systems face inefficiencies, requiring numerous strokes and significant computing resources, and often compromise 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 via a single pass, and render them on a canvas, utilizing a combination of losses to balance efficiency, accuracy, and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image stylizing systems use multiple passes and numerous strokes to reproduce digital images, then accuracy is improved, but computing resources and time are significantly increased
Solution Approach 1:
The patent segments the image reproduction task into multiple independent drawing strokes with specific parameters (position, size, color, orientation). Each stroke is generated independently by the neural network, allowing parallel processing while maintaining overall image accuracy. This segmentation enables the system to achieve high-fidelity reproduction without requiring sequential multiple passes through the entire network.
Solution Approach 2:
The patent performs preliminary action by pre-defining a structured set of stroke parameters and templates before the actual image reproduction. The neural network is trained beforehand to understand the mapping from input images to stroke sequences, enabling it to generate accurate stylized images in a single pass without requiring iterative refinement or multiple processing passes during inference.
2Manufacturing precision
If conventional systems use numerous drawing strokes to achieve accurate stylization, then rendering quality is improved, but device complexity and computing resources are increased
Solution Approach 1:
The patent changes parameters by representing complex image transformations through a simplified set of stroke parameters (position, size, color, orientation, curvature). Instead of manipulating entire image pixels or complex transformation matrices, the system controls a limited number of parameters per stroke, significantly reducing computational complexity while maintaining stylization accuracy through the neural network's learned parameter mappings.
3Manufacturing precision
If conventional image stylizing methods require multiple processing passes, then accuracy is maintained, but processing time is increased
Solution Approach 1:
The patent implements continuity of useful action by designing the neural network to generate the complete sequence of stroke parameters in a single continuous forward pass. The encoder-processorsdecoder architecture ensures that all stroke parameters for the entire stylized image are produced in one uninterrupted computation, eliminating the need for multiple discrete processing passes and reducing total processing time while maintaining image fidelity through the network's comprehensive feature extraction and reconstruction capabilities.
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.