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.

翻译：我们提出一种新颖方法，用于对艺术图像中的几何抽象与纹理进行交互式控制。以往的基于示例的风格化方法通常纠缠形状、纹理与色彩，而用于图像合成的生成方法要么对输入图像做出预设（例如仅允许人脸处理），要么无法提供精确的编辑控制。相比之下，我们的整体方法将输入图像空间分解为形状与高频细节（构成图像纹理的参数化表示），从而实现对色彩与纹理的独立控制。该表示中的每个参数均可调控可微分风格化滤镜管道的绘画属性。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、笔触以及表面浮雕与轮廓等绘画属性进行交互式全局与局部调整。此外，我们展示了基于参考图像与文本提示的参数空间优化纹理风格迁移，以及针对实时纹理分解的单风格与任意风格参数预测网络的训练。