The recent segmentation foundation model, Segment Anything Model (SAM), exhibits strong zero-shot segmentation capabilities, but it falls short in generating fine-grained precise masks. To address this limitation, we propose a novel zero-shot image matting model, called ZIM, with two key contributions: First, we develop a label converter that transforms segmentation labels into detailed matte labels, constructing the new SA1B-Matte dataset without costly manual annotations. Training SAM with this dataset enables it to generate precise matte masks while maintaining its zero-shot capability. Second, we design the zero-shot matting model equipped with a hierarchical pixel decoder to enhance mask representation, along with a prompt-aware masked attention mechanism to improve performance by enabling the model to focus on regions specified by visual prompts. We evaluate ZIM using the newly introduced MicroMat-3K test set, which contains high-quality micro-level matte labels. Experimental results show that ZIM outperforms existing methods in fine-grained mask generation and zero-shot generalization. Furthermore, we demonstrate the versatility of ZIM in various downstream tasks requiring precise masks, such as image inpainting and 3D NeRF. Our contributions provide a robust foundation for advancing zero-shot matting and its downstream applications across a wide range of computer vision tasks. The code is available at \url{https://github.com/naver-ai/ZIM}.

本研究针对现有分割基础模型在生成精确掩膜方面的不足，提出了一个新的零-shot图像抠图模型ZIM。关键创新在于开发了标签转换器来生成详细的掩膜标签，并通过构建新的SA1B-Matte数据集，使模型在无须昂贵人工标注的情况下能够生成精确掩膜。实验结果表明ZIM在细粒度掩膜生成和零-shot泛化方面优于现有方法，具有广泛的计算机视觉应用潜力。

ZIM：用于任意对象的零-shot图像抠图