This paper presents a novel free-hand sketch synthesis approach addressing explicit abstraction control in class-conditional and photo-to-sketch synthesis. Abstraction is a vital aspect of sketches, as it defines the fundamental distinction between a sketch and an image. Previous works relied on implicit control to achieve different levels of abstraction, leading to inaccurate control and synthesized sketches deviating from human sketches. To resolve this challenge, we propose two novel abstraction control mechanisms, state embeddings and the stroke token, integrated into a transformer-based latent diffusion model (LDM). These mechanisms explicitly provide the required amount of points or strokes to the model, enabling accurate point-level and stroke-level control in synthesized sketches while preserving recognizability. Outperforming state-of-the-art approaches, our method effectively generates diverse, non-rigid and human-like sketches. The proposed approach enables coherent sketch synthesis and excels in representing human habits with desired abstraction levels, highlighting the potential of sketch synthesis for real-world applications.

翻译：本文提出了一种新颖的手绘草图合成方法，解决了类别条件合成和照片到草图合成中的显式抽象控制问题。抽象是草图的关键特性，它定义了草图与图像之间的根本区别。以往的研究依赖隐式控制来实现不同抽象层次，导致控制不准确且合成的草图偏离人类手绘风格。为解决这一挑战，我们提出了两种新颖的抽象控制机制——状态嵌入和笔画标记，并将其集成到基于Transformer的潜在扩散模型（LDM）中。这些机制向模型显式提供所需的点或笔画数量，从而在保留可识别性的前提下，实现对合成草图中点级和笔画级的精确控制。我们的方法超越了现有最先进技术，能够有效生成多样化、非刚性且类似人类手绘的草图。所提出的方法实现了连贯的草图合成，并在以期望的抽象层次表现人类绘图习惯方面表现卓越，突显了草图合成在实际应用中的潜力。