Stylized Neural Painting (2011.08114v1)

Abstract: This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not differentiable, we design a novel neural renderer which imitates the behavior of the vector renderer and then frame the stroke prediction as a parameter searching process that maximizes the similarity between the input and the rendering output. We explored the zero-gradient problem on parameter searching and propose to solve this problem from an optimal transportation perspective. We also show that previous neural renderers have a parameter coupling problem and we re-design the rendering network with a rasterization network and a shading network that better handles the disentanglement of shape and color. Experiments show that the paintings generated by our method have a high degree of fidelity in both global appearance and local textures. Our method can be also jointly optimized with neural style transfer that further transfers visual style from other images. Our code and animated results are available at \url{https://jiupinjia.github.io/neuralpainter/}.

• The paper introduces a stroke-based rendering mechanism that formulates stroke prediction as a parameter search to maximize image similarity.
• It employs a neural renderer with distinct rasterization and shading networks to effectively address parameter coupling for realistic stroke representation.
• The study integrates neural style transfer with optimal transport loss, demonstrating superior texture clarity and diverse artistic effects across styles.

Insights into Stylized Neural Painting: A Technical Overview

The paper "Stylized Neural Painting" presents a significant advancement in the field of image-to-painting translation by introducing a novel method that leverages vectorized stroke rendering for creating vivid paintings with controllable styles. Unlike traditional pixel-based approaches, this method operates within a vectorized environment, producing meaningful stroke parameters for rendering. This approach significantly bridges the gap between artistic expression and computational techniques by emulating human painting processes.

Technical Contributions and Methodology

The authors introduce a stroke-based rendering mechanism that innovatively formulates stroke prediction as a parameter searching problem, maximizing the similarity between input images and final rendered outputs. This is executed in a differentiable manner using a neural renderer, which overcomes the non-differentiability of traditional vector rendering processes.

1. Neural Renderer Design: The architecture decomposes into a rasterization network and a shading network, adeptly handling shape and color disentanglement. This design addresses the parameter coupling issue evident in previous neural stroke renderers, enabling more accurate and realistic stroke representations.
2. Optimal Transport for Parameter Optimization: The paper identifies a zero-gradient problem in pixel-wise loss functions during stroke parameter optimization. This necessitates an optimal transport perspective to redefine the loss as a transportation process, effectively reducing the computational efforts required for parameter updates.
3. Neural Style Transfer Integration: The proposed system naturally integrates with neural style transfer frameworks, allowing the transfer of both visual styles and detailed artistic textures from reference images to the generated paintings.

Empirical Results

The experiments conducted demonstrate the proficiency of the "Stylized Neural Painter" in generating art that maintains both global realism and local texture fidelity. High-resolution results illustrate the capability of generating paintings with diverse styles, such as oil painting, watercolor, and marker pen effects. Comparative studies with contemporary methods, such as "Learning-to-Paint" and SPIRAL, show superior performance in terms of texture clarity and visual impact.

Implications and Future Research

The practical implications of this research present potential applications in digital art platforms and tools for artists and designers, providing them with sophisticated tools that automate certain aspects of painting without sacrificing artistic control. Theoretically, the paper also contributes to the paper of differentiable rendering techniques, which may inform future work on generalizing these methods to other artistic mediums and contexts.

In the landscape of artificial intelligence and its application to creative tasks, this method represents a stride towards more nuanced and controlled generation of artistic works, calling for further research into the intricacies of style representation and transfer. Future developments could explore adaptive learning mechanisms that personalize the painting style to individual artists' preferences or real-time applications in video and interactive media.

In conclusion, "Stylized Neural Painting" innovatively applies vector-based methodologies and optimal transport theories to the field of neural painting, setting a precedent for future explorations of machine learning applications in the generation of complex artistic tasks.