Matte Anything: Interactive Natural Image Matting with Segment Anything Models

Abstract: Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimap often requires significant labor, which limits the widespread application of matting algorithms on a large scale. To address the issue, we propose Matte Anything (MatAny), an interactive natural image matting model that could produce high-quality alpha-matte with various simple hints. The key insight of MatAny is to generate pseudo trimap automatically with contour and transparency prediction. In our work, we leverage vision foundation models to enhance the performance of natural image matting. Specifically, we use the segment anything model to predict high-quality contour with user interaction and an open-vocabulary detector to predict the transparency of any object. Subsequently, a pre-trained image matting model generates alpha mattes with pseudo trimaps. MatAny is the interactive matting algorithm with the most supported interaction methods and the best performance to date. It consists of orthogonal vision models without any additional training. We evaluate the performance of MatAny against several current image matting algorithms. MatAny has 58.3% improvement on MSE and 40.6% improvement on SAD compared to the previous image matting methods with simple guidance, achieving new state-of-the-art (SOTA) performance. The source codes and pre-trained models are available at https://github.com/hustvl/Matte-Anything.

• The paper presents an interactive natural image matting approach using Segment Anything Models to improve segmentation precision.
• It employs a robust LaTeX framework with standardized bibliography integration to support reproducible academic research.
• This methodology offers a scalable foundation for advancing automated image matting and visual segmentation studies.

Overview of the Document

The provided document is a LaTeX file, devoid of substantive content and primarily serving as a structural template for an academic paper. It utilizes the article class, indicating its design for academic submission, and it incorporates a bibliography, suggesting a framework for referencing scholarly work.

Technical Details

A noteworthy aspect of the document is the \bibliography{references} command, which points to an external bibliography file. This reflects a standardized approach in academic writing, facilitating the integration of a citation database. The designated bibliography style is splncs04, commonly employed for conferences associated with the Lecture Notes in Computer Science (LNCS) series.

Implications and Considerations

The framework outlined in this document is foundational for organizing and presenting detailed research findings in a structured manner. It illustrates the typical process of integrating literature review and citations within the context of an academic paper. This structure is critical for ensuring consistency and reproducibility in scientific communication.

Future Directions

As academic publishing evolves, the template's relevance persists, though adaptations to newer paradigms such as digital-first publication and open access may be necessary. The underlying LaTeX framework remains robust, offering high-quality typesetting and precision in scholarly documentation. Future developments could enhance integration with tools for automated bibliography management and more seamless collaboration in distributed research environments.

In summary, while providing minimal substantive content, the document serves as an essential template for the formal presentation of academic research, underscoring the importance of structure in scholarly discourse.