✦
Summary
VideoMaMa, or Video Mask-to-Matte Model, is an innovative approach to video matting that transforms coarse segmentation masks into highly precise alpha mattes without requiring labeled real-world video data. Trained exclusively on synthetic datasets, it leverages powerful pretrained video diffusion models as generative priors to achieve exceptional zero-shot generalization across diverse real-world footage, including complex scenes with varied motions, lighting conditions, and object interactions. This method addresses the longstanding challenge in video matting by enabling robust performance on in-the-wild videos where traditional models falter due to domain gaps and data scarcity.
Key Features
Converts coarse segmentation masks into pixel-accurate alpha mattes using pretrained video diffusion priors.
Achieves strong zero-shot generalization to real-world videos trained only on synthetic data.
Develops a scalable pseudo-labeling pipeline for automatic high-quality video matting annotations.
Introduces the MA-V dataset with over 50K real-world videos featuring diverse scenes and motions.
Fine-tunes SAM2 into SAM2-Matte, outperforming baselines on in-the-wild video matting tasks.
Ensures temporal consistency and detail preservation through mask-guided diffusion refinement.
Supports interactive demos for video selection and in-the-wild result visualization.
Provides quantitative and qualitative comparisons validating superior performance metrics.
About VideoMaMa
Building upon VideoMaMa's strong capabilities, the framework introduces a scalable pseudo-labeling pipeline that generates high-quality matting annotations automatically from accessible segmentation cues. This pipeline facilitates the creation of the Matting Anything in Video (MA-V) dataset, comprising over 50,000 real-world videos annotated with pixel-accurate alpha mattes, covering a broad spectrum of everyday scenarios, dynamic movements, and environmental variations. By democratizing access to large-scale training data, VideoMaMa paves the way for advancing video editing tools, compositing workflows, and augmented reality applications that demand seamless foreground-background separation.
To demonstrate practical impact, VideoMaMa fine-tunes the Segment Anything Model 2 (SAM2) on the MA-V dataset, resulting in SAM2-Matte, which exhibits superior robustness and accuracy on unseen in-the-wild videos compared to models trained on prior matting datasets. The architecture integrates mask-guided processing with diffusion-based refinement, ensuring temporal consistency and fine-grained detail preservation across video frames. All models, code, and the comprehensive MA-V dataset are set for public release, empowering researchers and developers to push the boundaries of generative video processing and scalable annotation strategies.
