title: MatAnyone: Stable Video Matting with Consistent Memory Propagation
summary: 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_authors._id: 679d5057ca02e3270aaada16
paper_authors.name: Peiqing Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T16:54:33.485Z
paper_authors._id: 6513aae6330c55fdc5462ca8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/EDhpiTqCBMNPmMGrOKcvY.jpeg
paper_authors.fullname: pq-yang
paper_authors.type: user
paper_authors.user: PeiqingYang
paper_authors._id: 679d5057ca02e3270aaada17
paper_authors.name: Shangchen Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T16:54:35.247Z
paper_authors._id: 62e57662ae9d3f10acbb1b1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62e57662ae9d3f10acbb1b1b/lg58jdbNyv6LGH2LFnZDF.png
paper_authors.fullname: Shangchen Zhou
paper_authors.type: user
paper_authors.user: sczhou
paper_authors._id: 679d5057ca02e3270aaada18
paper_authors.name: Jixin Zhao
paper_authors._id: 679d5057ca02e3270aaada19
paper_authors.name: Qingyi Tao
paper_authors._id: 679d5057ca02e3270aaada1a
paper_authors.name: Chen Change Loy
paper_title: MatAnyone: Stable Video Matting with Consistent Memory Propagation
paper_summary: 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_ai_keywords: memory-based paradigm memory propagation module region-adaptive memory fusion video matting semantic stability fine-grained details training strategy large-scale segmentation data matting stability