Conditional Human Sketch Synthesis with Explicit Abstraction Control

Abstract

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.