Abstract
Real-world image matting is essential for applications in content creation and augmented reality. However, it remains challenging due to the complex nature of scenes and the scarcity of high-quality datasets. To address these limitations, we introduce Mask2Alpha, an iterative refinement framework designed to enhance semantic comprehension, instance awareness, and fine-detail recovery in image matting. Our framework leverages self-supervised Vision Transformer features as semantic priors, strengthening contextual understanding in complex scenarios. To further improve instance differentiation, we implement a mask-guided feature selection module, enabling precise targeting of objects in multi-instance settings. Additionally, a sparse convolution-based optimization scheme allows Mask2Alpha to recover high-resolution details through progressive refinement,from low-resolution semantic passes to high-resolution sparse reconstructions. Benchmarking across various real-world datasets, Mask2Alpha consistently achieves state-of-the-art results, showcasing its effectiveness in accurate and efficient image matting.
Abstract (translated)
真实世界的图像抠图对于内容创作和增强现实应用至关重要。然而，由于场景的复杂性和高质量数据集的稀缺性，这一任务仍然具有挑战性。为了克服这些限制，我们引入了Mask2Alpha，这是一个迭代改进框架，旨在提升语义理解、实例感知以及细节点恢复方面的能力。我们的框架利用自监督视觉变换器特征作为先验知识，从而增强了在复杂场景中的上下文理解能力。此外，为提高对不同实例的区分度，我们实施了一个基于掩码引导的特征选择模块，使得在多实例情况下能够精准定位目标对象。另外，Mask2Alpha通过采用一种基于稀疏卷积优化方案，从低分辨率语义通道到高分辨率稀疏重建进行逐步细化，从而恢复出高质量细节。 经过多个真实世界数据集上的基准测试，Mask2Alpha始终展现出卓越的性能，证明了其在图像抠图准确性与效率方面的有效性。
URL
https://arxiv.org/abs/2502.17093
