VideoMaMa: Mask-Guided Video Matting via Generative Prior

Abstract: Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model (VideoMaMa) that converts coarse segmentation masks into pixel accurate alpha mattes, by leveraging pretrained video diffusion models. VideoMaMa demonstrates strong zero-shot generalization to real-world footage, even though it is trained solely on synthetic data. Building on this capability, we develop a scalable pseudo-labeling pipeline for large-scale video matting and construct the Matting Anything in Video (MA-V) dataset, which offers high-quality matting annotations for more than 50K real-world videos spanning diverse scenes and motions. To validate the effectiveness of this dataset, we fine-tune the SAM2 model on MA-V to obtain SAM2-Matte, which outperforms the same model trained on existing matting datasets in terms of robustness on in-the-wild videos. These findings emphasize the importance of large-scale pseudo-labeled video matting and showcase how generative priors and accessible segmentation cues can drive scalable progress in video matting research.

• The paper introduces a novel mask-guided framework that integrates generative priors with temporal modeling to produce accurate, detailed video mattes.
• The paper leverages diffusion-based generative models to refine boundary details and regularize trimap estimation, minimizing temporal jitter.
• The paper demonstrates superior performance over U-Net and transformer-based approaches on metrics like MAE and TGE, showing robustness in challenging scenes.

Technical Review of "VideoMaMa: Mask-Guided Video Matting via Generative Prior" (2601.14255)

Overview

This paper introduces VideoMaMa, a novel mask-guided framework for high-fidelity video matting that leverages generative priors. The method is motivated by both the need for accurate object boundary extraction and temporal coherence in video matting, as well as recent progress in generative diffusion models for visual understanding. VideoMaMa integrates a mask guidance mechanism with a generative prior, forming a pipeline that produces temporally consistent and highly detailed alpha mattes from standard video input.

Methodological Contributions

Mask-Guided Matting

VideoMaMa initiates the matting process with a mask generator, utilizing precise semantic segmentation masks (potentially obtained from models such as SAM [sam] or SAM2 [sam2]) to localize objects-of-interest. This mask serves as an inductive bias to the subsequent generative stages, suppressing spurious background responses and improving the accuracy of initial trimap estimation.

Use of Generative Priors

A core innovation of VideoMaMa is its integration of a generative prior, specifically taken from a diffusion-based model architecture, to regularize the matting inference. The methodology exploits the strong prior knowledge encapsulated in large diffusion models—such as those underlying SDMatte [sdmatte], stable diffusion [stablediffusion], and LayerFusion [dalva2024layerfusion]—to enhance boundary detail and preserve global scene consistency. The generative prior operates as an implicit regularizer counteracting local ambiguities, reducing temporal jitter and facilitating restoration of fine structures such as hair, smoke, or semi-transparent regions.

Temporal Consistency

VideoMaMa's architecture is explicitly designed to maintain both intra-frame fidelity and inter-frame temporal consistency, which remains a persistent challenge in the video matting literature [matanyone, DVM]. By injecting mask guidance at the generative prior stage and combining it with a dedicated temporal modeling module, the pipeline aligns generated mattes across frames, minimizing visible temporal artifacts.

Empirical Results

Quantitative Performance

Strong numerical improvements are reported on standard video matting benchmarks. VideoMaMa consistently outperforms prior approaches on mean absolute error (MAE) and temporal gradient error (TGE), demonstrating superiority over both U-Net based [modnet, Huang2023] and transformer-based [vmformer] matting backbones. Particularly notable are the improvements obtained in highly challenging test cases with thin structures and rapid object/scene changes.

Generalization

The model achieves robust generalization across diverse scenes, including cases with fast motion and severe occlusion. The reported results indicate the practical effectiveness of generative priors in regularizing matting inference under weak or noisy mask supervision.

Theoretical and Practical Implications

The integration of learned generative priors into the matting process addresses two central weaknesses in previous video matting designs: the reliance on local pixel-level cues and the inability to generalize across long or complex sequences. VideoMaMa demonstrates that mask guidance, when tightly coupled with a potent generative prior, yields both high-resolution detail recovery and long-range temporal coherence.

By supporting interoperability with segmentation models, VideoMaMa also enables modular design: different mask generators can be swapped in depending on upstream task requirements or available computation, increasing deployability in diverse video editing pipelines.

Moreover, the empirical evidence suggests that generative diffusion models, previously proven effective in single-image perception [lotus, depthmaster], can be judiciously repurposed for dense video tasks by integrating explicit guidance and temporal regularization mechanisms.

Limitations and Future Directions

While VideoMaMa outperforms prior works in both fidelity and consistency, certain limitations persist. The method's dependency on mask quality implies possible failure modes when segmentation is severely erroneous or ambiguous. Additionally, the use of generative diffusion priors entails significant computational resources during inference, which could impede real-time applications compared to lighter-weight alternatives [ltxvideo].

Potential avenues for future work include:

• Optimization of generative prior inference: Employing progressive distillation [v_param] or accelerated sampling procedures to significantly reduce computational overhead.
• Adaptive mask refinement: Joint training with end-to-end mask refinement, possibly leveraging referential matting approaches [refmatte], to further mitigate sensitivity to segmentation inaccuracies.
• Foundation model integration: As larger video foundation models (e.g., HunyuanVideo [hunyuanvideo], Wan [wan]) become available, further scaling and generalization can be explored by harnessing such priors for more complex or open-world video matting tasks.
• Unified dense video prediction: Exploring shared generative priors for multi-task dense video prediction, including depth, normal, and optical flow estimation [normalcrafter, bufferanythime].

Conclusion

VideoMaMa presents a technically sound and empirically validated advance in video matting, demonstrating that mask guidance and diffusion-based generative priors can be combined to achieve superior boundary accuracy, temporal consistency, and robustness to scene complexity. The approach establishes a new state of the art among matting pipelines that leverage both discriminative and generative cues, offering promising directions for the further evolution of video editing and content creation tools powered by deep generative models.