SketchRef: A Benchmark Dataset and Evaluation Metrics for Automated Sketch Synthesis
<|reference_start|>SketchRef: A Benchmark Dataset and Evaluation Metrics for Automated Sketch Synthesis: Sketch, a powerful artistic technique to capture essential visual information about real-world objects, is increasingly gaining attention in the image synthesis field. However, evaluating the quality of synthesized sketches presents unique unsolved challenges. Current evaluation methods for sketch synthesis are inadequate due to the lack of a unified benchmark dataset, over-reliance on classification accuracy for recognizability, and unfair evaluation of sketches with different levels of simplification. To address these issues, we introduce SketchRef, a benchmark dataset comprising 4 categories of reference photos--animals, human faces, human bodies, and common objects--alongside novel evaluation metrics. Considering that classification accuracy is insufficient to measure the structural consistency between a sketch and its reference photo, we propose the mean Object Keypoint Similarity (mOKS) metric, utilizing pose estimation to assess structure-level recognizability. To ensure fair evaluation sketches with different simplification levels, we propose a recognizability calculation method constrained by simplicity. We also collect 8K responses from art enthusiasts, validating the effectiveness of our proposed evaluation methods. We hope this work can provide a comprehensive evaluation of sketch synthesis algorithms, thereby aligning their performance more closely with human understanding.<|reference_end|>
@article{lin2024sketchref:,
    title={SketchRef: A Benchmark Dataset and Evaluation Metrics for Automated
  Sketch Synthesis},
    author={Xingyue Lin, Xingjian Hu, Shuai Peng, Jianhua Zhu, Liangcai Gao},
    journal={arXiv preprint arXiv:2408.08623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08623},
    primaryClass={cs.CV cs.AI}
}