MatAnyone: Stable Video Matting with Consistent Memory Propagation
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.
679d5057ca02e3270aaada16
Peiqing Yang
claimed_verified
2025-02-04T16:54:33.485Z
6513aae6330c55fdc5462ca8
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/EDhpiTqCBMNPmMGrOKcvY.jpeg
pq-yang
user
PeiqingYang
679d5057ca02e3270aaada17
Shangchen Zhou
claimed_verified
2025-02-04T16:54:35.247Z
62e57662ae9d3f10acbb1b1b
https://cdn-avatars.huggingface.co/v1/production/uploads/62e57662ae9d3f10acbb1b1b/lg58jdbNyv6LGH2LFnZDF.png
Shangchen Zhou
user
sczhou
679d5057ca02e3270aaada18
Jixin Zhao
claimed_verified
2025-07-04T07:52:00.727Z
6645ede352f167b48fd23198
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Jixin Zhao
user
jixin0101
679d5057ca02e3270aaada19
Qingyi Tao
679d5057ca02e3270aaada1a
Chen Change Loy
MatAnyone: Stable Video Matting with Consistent Memory Propagation
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.
MatAnyone, a memory-based video matting framework, uses region-adaptive memory fusion for robust and accurate matting in complex scenarios.
memory-based paradigm region-adaptive memory fusion semantic stability fine-grained details segmentation data