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MatAnyone: Stable Video Matting with Consistent Memory Propagation

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arxiv 2501.14677 v2 pith:ZJ5BYPUG submitted 2025-01-24 cs.CV

Peiqing Yang , Shangchen Zhou , Jixin Zhao , Qingyi Tao , Chen Change Loy This is my paper
classification cs.CV
keywords mattingvideomemorymatanyonerobusttrainingconsistentdataset
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Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust framework tailored for target-assigned video matting. Specifically, building on a memory-based paradigm, we introduce a consistent memory propagation module via region-adaptive memory fusion, which adaptively integrates memory from the previous frame. This ensures semantic stability in core regions while preserving fine-grained details along object boundaries. For robust training, we present a larger, high-quality, and diverse dataset for video matting. Additionally, we incorporate a novel training strategy that efficiently leverages large-scale segmentation data, boosting matting stability. With this new network design, dataset, and training strategy, MatAnyone delivers robust and accurate video matting results in diverse real-world scenarios, outperforming existing methods.

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Cited by 3 Pith papers

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    End-to-end video diffusion framework with tri-mask guidance and RGB-D denoising for joint modeling of character-to-environment physical interactions and environment-to-character lighting harmonization in cinematic com...

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