Conditional Human Sketch Synthesis with Explicit Abstraction Control
Dar-Yen Chen
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.