Facial sketch synthesis (FSS) aims to generate a vivid sketch portrait from a given facial photo. Existing FSS methods merely rely on 2D representations of facial semantic or appearance. However, professional human artists usually use outlines or shadings to covey 3D geometry. Thus facial 3D geometry (e.g. depth map) is extremely important for FSS. Besides, different artists may use diverse drawing techniques and create multiple styles of sketches; but the style is globally consistent in a sketch. Inspired by such observations, in this paper, we propose a novel Human-Inspired Dynamic Adaptation (HIDA) method. Specially, we propose to dynamically modulate neuron activations based on a joint consideration of both facial 3D geometry and 2D appearance, as well as globally consistent style control. Besides, we use deformable convolutions at coarse-scales to align deep features, for generating abstract and distinct outlines. Experiments show that HIDA can generate high-quality sketches in multiple styles, and significantly outperforms previous methods, over a large range of challenging faces. Besides, HIDA allows precise style control of the synthesized sketch, and generalizes well to natural scenes and other artistic styles. Our code and results have been released online at: https://github.com/AiArt-HDU/HIDA.

翻译：人脸素描合成（FSS）旨在从给定人脸照片生成逼真的素描肖像。现有FSS方法仅依赖人脸语义或外观的二维表征，然而专业人类艺术家通常通过轮廓线或阴影来传达三维几何信息。因此，人脸三维几何（如深度图）对FSS至关重要。此外，不同艺术家可能采用各异绘画技法创作多种风格素描，但同一幅素描中的风格具有全局一致性。受此启发，本文提出一种新颖的受人类启发的动态自适应（HIDA）方法。具体而言，我们提出基于人脸三维几何与二维外观的联合考量及全局一致风格控制，动态调节神经元激活。同时，在粗尺度上采用可变形卷积对齐深度特征，以生成抽象且清晰的轮廓线。实验表明，HIDA能生成高质量的多风格素描，在大量具有挑战性的人脸数据上显著优于先前方法。此外，HIDA可实现合成素描的精确风格控制，并能良好迁移至自然场景及其他艺术风格。我们的代码与结果已在https://github.com/AiArt-HDU/HIDA 开源。