title: Segment Any Motion in Videos
summary: Moving object segmentation is a crucial task for achieving a high-level
  understanding of visual scenes and has numerous downstream applications. Humans
  can effortlessly segment moving objects in videos. Previous work has largely
  relied on optical flow to provide motion cues; however, this approach often
  results in imperfect predictions due to challenges such as partial motion,
  complex deformations, motion blur and background distractions. We propose a
  novel approach for moving object segmentation that combines long-range
  trajectory motion cues with DINO-based semantic features and leverages SAM2 for
  pixel-level mask densification through an iterative prompting strategy. Our
  model employs Spatio-Temporal Trajectory Attention and Motion-Semantic
  Decoupled Embedding to prioritize motion while integrating semantic support.
  Extensive testing on diverse datasets demonstrates state-of-the-art
  performance, excelling in challenging scenarios and fine-grained segmentation
  of multiple objects. Our code is available at https://motion-seg.github.io/.
paper_authors._id: 67e9fec1564b123aa5d70388
paper_authors.name: Nan Huang
paper_authors._id: 67e9fec1564b123aa5d70389
paper_authors.name: Wenzhao Zheng
paper_authors._id: 67e9fec1564b123aa5d7038a
paper_authors.name: Chenfeng Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T09:56:25.025Z
paper_authors._id: 654ca71d5255ee86711b52c5
paper_authors.avatarUrl: /avatars/52bf00fd74c8db5643c4daa185c678e6.svg
paper_authors.fullname: Chenfeng Xu
paper_authors.type: user
paper_authors.user: chenfengx
paper_authors._id: 67e9fec1564b123aa5d7038b
paper_authors.name: Kurt Keutzer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T09:56:18.832Z
paper_authors._id: 6251bf4b183aa4266924ad91
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1678041834400-6251bf4b183aa4266924ad91.jpeg
paper_authors.fullname: Kurt Keutzer
paper_authors.type: user
paper_authors.user: kurtkeutzer
paper_authors._id: 67e9fec1564b123aa5d7038c
paper_authors.name: Shanghang Zhang
paper_authors._id: 67e9fec1564b123aa5d7038d
paper_authors.name: Angjoo Kanazawa
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T09:56:03.262Z
paper_authors._id: 6478cf7150ff7001631679c3
paper_authors.avatarUrl: /avatars/65ec385a9cc44c972e6caf952e759ff1.svg
paper_authors.fullname: Angjoo Kanazawa
paper_authors.type: user
paper_authors.user: akanazawa
paper_authors._id: 67e9fec1564b123aa5d7038e
paper_authors.name: Qianqian Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T09:55:54.563Z
paper_authors._id: 6616f0c4c2e30710e607c2bf
paper_authors.avatarUrl: /avatars/a5941e0ed940439f4c7c67747318cbfc.svg
paper_authors.fullname: Qianqian Wang
paper_authors.type: user
paper_authors.user: qianqian68
paper_title: Segment Any Motion in Videos
paper_summary: Moving object segmentation is a crucial task for achieving a high-level
  understanding of visual scenes and has numerous downstream applications. Humans
  can effortlessly segment moving objects in videos. Previous work has largely
  relied on optical flow to provide motion cues; however, this approach often
  results in imperfect predictions due to challenges such as partial motion,
  complex deformations, motion blur and background distractions. We propose a
  novel approach for moving object segmentation that combines long-range
  trajectory motion cues with DINO-based semantic features and leverages SAM2 for
  pixel-level mask densification through an iterative prompting strategy. Our
  model employs Spatio-Temporal Trajectory Attention and Motion-Semantic
  Decoupled Embedding to prioritize motion while integrating semantic support.
  Extensive testing on diverse datasets demonstrates state-of-the-art
  performance, excelling in challenging scenarios and fine-grained segmentation
  of multiple objects. Our code is available at https://motion-seg.github.io/.
paper_ai_summary: A novel approach combining long-range trajectory motion cues with DINO-based semantic features and SAM2 for moving object segmentation achieves state-of-the-art performance in challenging scenarios.
paper_ai_keywords: DINO SAM2 Spatio-Temporal Trajectory Attention Motion-Semantic Decoupled Embedding