Enhancing Image Matting in Real-World Scenes with Mask-Guided Iterative Refinement
This work addresses image matting for content creation and augmented reality, presenting an incremental improvement with novel components for better performance.
The paper tackled the challenge of real-world image matting by introducing Mask2Alpha, an iterative refinement framework that enhances semantic comprehension, instance awareness, and fine-detail recovery, achieving state-of-the-art results across various datasets.
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.