🤖 AI Summary
Real-world image matting is hindered by weak semantic understanding, difficulty in distinguishing multiple foreground instances, poor fine-detail recovery, and scarcity of high-quality annotated data. To address these challenges, we propose Mask2Alpha, an iterative refinement framework that introduces a mask-guided feature selection mechanism and a sparse convolution-based progressive optimization strategy, integrated with semantic priors extracted via self-supervised Vision Transformers (ViTs). Our method enables fine-grained instance-aware matting and high-resolution alpha matte reconstruction within a multi-stage iterative architecture—without requiring additional manual annotations—thereby enhancing model generalization. Evaluated on multiple real-world benchmarks, Mask2Alpha achieves state-of-the-art performance, notably improving matting accuracy (e.g., +0.8% F-measure on Composition-1k) and inference efficiency (1.7× faster than baseline models). The framework delivers an efficient, robust, end-to-end solution for content creation and AR applications.
📝 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.