arXiv:2502.09608cs.CVcs.GR2025-02International Conf…被引 4

用自然图像先验实现手绘场景草图的实例分割,支持精准编辑。

Instance Segmentation of Scene Sketches Using Natural Image Priors

  • 基于无类别微调与深度线索优化分割结果
  • 在自建数据集上实现多风格草图的稳定分割
  • 输出分层草图,支持遮挡修复与高级编辑

草图分割旨在将属于同一对象或实例的像素进行分组,是实现草图移动、缩放或删除等编辑任务的重要工具。由于草图具有稀疏性且风格差异大,现有图像分割模型难以直接应用。本文提出InkLayer方法,通过类无关微调和深度线索精炼分割掩码,将先进图像分割与目标检测模型适配至草图领域。同时,该方法将草图组织为有序层级,对被遮挡部分进行填充,支持高级草图编辑。针对现有数据集风格单一的问题,我们构建了合成数据集InkScenes,包含多样笔触与细节层次的场景草图,并以此验证方法的鲁棒性。

原文摘要 · Abstract (English)

Sketch segmentation involves grouping pixels within a sketch that belong to the same object or instance. It serves as a valuable tool for sketch editing tasks, such as moving, scaling, or removing specific components. While image segmentation models have demonstrated remarkable capabilities in recent years, sketches present unique challenges for these models due to their sparse nature and wide variation in styles. We introduce InkLayer, a method for instance segmentation of raster scene sketches. Our approach adapts state-of-the-art image segmentation and object detection models to the sketch domain by employing class-agnostic fine-tuning and refining segmentation masks using depth cues. Furthermore, our method organizes sketches into sorted layers, where occluded instances are inpainted, enabling advanced sketch editing applications. As existing datasets in this domain lack variation in sketch styles, we construct a synthetic scene sketch segmentation dataset, InkScenes, featuring sketches with diverse brush strokes and varying levels of detail. We use this dataset to demonstrate the robustness of our approach.

草图分割实例分割图像生成深度线索

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