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<p>Sketch reconstruction aims to recreate a target sketch by generating a sequence of vector strokes. Traditional methods often focus solely on the visual similarity of the final drawing while neglecting the stroke generation process, resulting in redundant strokes and disordered sequences. To address this limitation, we propose a sketch agent framework based on a Constrained Markov Decision Process (CMDP). To ensure the spatial continuity between adjacent strokes and get closer to the human drawing process, we introduce a hybrid action space for the sketching agent. Furthermore, we carefully design reward and cost functions to guide the agent in achieving efficient sketch reconstruction using more concise strokes while maintaining visual fidelity. Unlike existing methods that rely on supervised learning, our framework adopts a self-supervised learning paradigm, freeing it from the dependence on paired vector label data. Experimental results on the MNIST and QuickDraw datasets demonstrate the significant advantages of our approach in various sketch reconstruction tasks. Ablation studies further validate the effectiveness of our method in reducing the number of strokes and optimizing their sequence. More results can be found at: <a href="https://chenyinlin2.github.io/Sketching_Agent/">https://chenyinlin2.github.io/Sketching_Agent/</a></p>

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Sketching agent: learning concise and efficient stroke sequences for sketch reconstruction

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Gaofeng Liu,
  • Jian Liu,
  • Xuetong Li,
  • Yongqi Shao,
  • Hong Huo,
  • Tao Fang

摘要

Sketch reconstruction aims to recreate a target sketch by generating a sequence of vector strokes. Traditional methods often focus solely on the visual similarity of the final drawing while neglecting the stroke generation process, resulting in redundant strokes and disordered sequences. To address this limitation, we propose a sketch agent framework based on a Constrained Markov Decision Process (CMDP). To ensure the spatial continuity between adjacent strokes and get closer to the human drawing process, we introduce a hybrid action space for the sketching agent. Furthermore, we carefully design reward and cost functions to guide the agent in achieving efficient sketch reconstruction using more concise strokes while maintaining visual fidelity. Unlike existing methods that rely on supervised learning, our framework adopts a self-supervised learning paradigm, freeing it from the dependence on paired vector label data. Experimental results on the MNIST and QuickDraw datasets demonstrate the significant advantages of our approach in various sketch reconstruction tasks. Ablation studies further validate the effectiveness of our method in reducing the number of strokes and optimizing their sequence. More results can be found at: https://chenyinlin2.github.io/Sketching_Agent/