SketchRef: a Multi-Task Evaluation Benchmark for Sketch Synthesis
Xingyue Lin, Xingjian Hu, Shuai Peng, Jianhua Zhu, Liangcai Gao

TL;DR
SketchRef is a comprehensive benchmark for evaluating sketch synthesis methods, introducing new tasks, metrics, and a large dataset to standardize and advance research in the field.
Contribution
It presents the first multi-task benchmark for sketch synthesis, including novel evaluation metrics and a large annotated dataset across multiple domains.
Findings
Existing methods show varied strengths and weaknesses on the benchmark.
The recognizability-simplicity trade-off can be quantitatively assessed.
The benchmark enables standardized evaluation of sketch synthesis algorithms.
Abstract
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…
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Taxonomy
TopicsInteractive and Immersive Displays · Architecture and Computational Design · Human Motion and Animation
MethodsSoftmax · Attention Is All You Need
