控制艺术图像的几何抽象与纹理
Controlling Geometric Abstraction and Texture for Artistic Images
AI总结：
提出一种交互式控制艺术图像几何抽象与纹理的方法，通过空间分解输入为形状与参数化纹理表示，实现颜色、形状和绘画属性的独立编辑，并支持基于优化的风格迁移与实时纹理分解网络。
摘要：
我们提出了一种交互式控制艺术图像中几何抽象与纹理的新方法。以往基于示例的风格化方法通常将形状、纹理和颜色纠缠在一起，而用于图像合成的生成式方法通常要么对输入图像做出假设（例如仅允许人脸），要么不提供精确的编辑控制。相比之下，我们的整体方法在空间上将输入分解为形状和包含图像纹理的高频细节的参数化表示，从而实现对颜色和纹理的独立控制。该表示中的每个参数控制着一系列可微风格化滤波器管线的绘画属性。所提出的形状与纹理解耦支持多种风格化编辑选项，包括对形状、笔触以及表面浮雕和轮廓等绘画属性的交互式全局与局部调整。此外，我们展示了使用参考图像和文本提示在参数空间中进行基于优化的纹理风格迁移，以及训练单一样式和任意样式参数预测网络以实现实时纹理分解。
英文摘要：
We present a novel method for the interactive control of geometric abstraction and texture in artistic images. Previous example-based stylization methods often entangle shape, texture, and color, while generative methods for image synthesis generally either make assumptions about the input image, such as only allowing faces or do not offer precise editing controls. By contrast, our holistic approach spatially decomposes the input into shapes and a parametric representation of high-frequency details comprising the image's texture, thus enabling independent control of color and texture. Each parameter in this representation controls painterly attributes of a pipeline of differentiable stylization filters. The proposed decoupling of shape and texture enables various options for stylistic editing, including interactive global and local adjustments of shape, stroke, and painterly attributes such as surface relief and contours. Additionally, we demonstrate optimization-based texture style-transfer in the parametric space using reference images and text prompts, as well as the training of single- and arbitrary style parameter prediction networks for real-time texture decomposition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
