MatAnyone: Stable Video Matting with Consistent Memory Propagation

pq-yang, Shangchen Zhou, Jixin Zhao, Qingyi Tao, Chen Change Loy

MatAnyone: Stable Video Matting with Consistent Memory Propagation: 32 upvotes on Hugging Face Daily Papers, #3 of 16 papers on 2025-02-03. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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