标题： 草图参考：草图合成的多任务评估基准 显示英文标题

标题： SketchRef: a Multi-Task Evaluation Benchmark for Sketch Synthesis

摘要： 速写是一种强大的艺术技巧，用于捕捉关于真实世界物体的关键视觉信息，并且在图像合成研究中越来越受到关注。然而，该领域缺乏一个统一的基准来评估各种合成方法的性能。为了解决这个问题，我们提出了SketchRef，这是第一个全面的多任务速写合成评估基准。SketchRef充分利用了速写与参考照片之间的共享特征。它引入了两个主要任务：类别预测和结构一致性估计，其中后者在以往的研究中很大程度上被忽视了。这些任务进一步分为四个领域的五个子任务：动物、常见物品、人体和面部。认识到速写中可识别性和简洁性之间的固有权衡，我们首次通过引入一种受简洁性约束的可识别性计算方法mRS来量化这种平衡，以确保公平和有意义的评估。为了验证我们的方法，我们收集了来自艺术爱好者的7,920份反馈，确认了我们提出的评估指标的有效性。此外，我们在基准上评估了现有的速写合成方法的表现，突出了它们的优点和缺点。我们希望通过这项研究建立一个标准化的基准，并为推进速写合成算法提供有价值的见解。 显示英文摘要

摘要： Sketching is a powerful artistic technique for capturing essential visual information about real-world objects and has increasingly attracted attention in image synthesis research. However, the field lacks a unified benchmark to evaluate the performance of various synthesis methods. To address this, we propose SketchRef, the first comprehensive multi-task evaluation benchmark for sketch synthesis. SketchRef fully leverages the shared characteristics between sketches and reference photos. It introduces two primary tasks: category prediction and structural consistency estimation, the latter being largely overlooked in previous studies. These tasks are further divided into five sub-tasks across four domains: animals, common things, human body, and faces. Recognizing the inherent trade-off between recognizability and simplicity in sketches, we are the first to quantify this balance by introducing a recognizability calculation method constrained by simplicity, mRS, ensuring fair and meaningful evaluations. To validate our approach, we collected 7,920 responses from art enthusiasts, confirming the effectiveness of our proposed evaluation metrics. Additionally, we evaluate the performance of existing sketch synthesis methods on our benchmark, highlighting their strengths and weaknesses. We hope this study establishes a standardized benchmark and offers valuable insights for advancing sketch synthesis algorithms.