title: Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
    Sparse Time-Variant Attribute Modeling
summary: Rendering dynamic scenes from monocular videos is a crucial yet challenging
  task. The recent deformable Gaussian Splatting has emerged as a robust solution
  to represent real-world dynamic scenes. However, it often leads to heavily
  redundant Gaussians, attempting to fit every training view at various time
  steps, leading to slower rendering speeds. Additionally, the attributes of
  Gaussians in static areas are time-invariant, making it unnecessary to model
  every Gaussian, which can cause jittering in static regions. In practice, the
  primary bottleneck in rendering speed for dynamic scenes is the number of
  Gaussians. In response, we introduce Efficient Dynamic Gaussian Splatting
  (EDGS), which represents dynamic scenes via sparse time-variant attribute
  modeling. Our approach formulates dynamic scenes using a sparse anchor-grid
  representation, with the motion flow of dense Gaussians calculated via a
  classical kernel representation. Furthermore, we propose an unsupervised
  strategy to efficiently filter out anchors corresponding to static areas. Only
  anchors associated with deformable objects are input into MLPs to query
  time-variant attributes. Experiments on two real-world datasets demonstrate
  that our EDGS significantly improves the rendering speed with superior
  rendering quality compared to previous state-of-the-art methods.
paper_authors._id: 67c1aa781c3a8036977ed8b1
paper_authors.name: Hanyang Kong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-28T15:13:50.949Z
paper_authors._id: 6442882f8443bce4c98a88aa
paper_authors.avatarUrl: /avatars/70d5aa651b07b43629554096d76efd4c.svg
paper_authors.fullname: Kong
paper_authors.type: user
paper_authors.user: imsuperkong
paper_authors._id: 67c1aa781c3a8036977ed8b2
paper_authors.name: Xingyi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:45:42.180Z
paper_authors._id: 634cfebc350bcee9bed20a4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
paper_authors.fullname: Xingyi Yang
paper_authors.type: user
paper_authors.user: adamdad
paper_authors._id: 67c1aa781c3a8036977ed8b3
paper_authors.name: Xinchao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-28T12:45:56.197Z
paper_authors._id: 63fc03a50aab060792ffef39
paper_authors.avatarUrl: /avatars/9d5b1bb2a41928e08176b703935133ab.svg
paper_authors.fullname: Wangxinchao
paper_authors.type: user
paper_authors.user: wxcTest
paper_title: Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
    Sparse Time-Variant Attribute Modeling
paper_summary: Rendering dynamic scenes from monocular videos is a crucial yet challenging
  task. The recent deformable Gaussian Splatting has emerged as a robust solution
  to represent real-world dynamic scenes. However, it often leads to heavily
  redundant Gaussians, attempting to fit every training view at various time
  steps, leading to slower rendering speeds. Additionally, the attributes of
  Gaussians in static areas are time-invariant, making it unnecessary to model
  every Gaussian, which can cause jittering in static regions. In practice, the
  primary bottleneck in rendering speed for dynamic scenes is the number of
  Gaussians. In response, we introduce Efficient Dynamic Gaussian Splatting
  (EDGS), which represents dynamic scenes via sparse time-variant attribute
  modeling. Our approach formulates dynamic scenes using a sparse anchor-grid
  representation, with the motion flow of dense Gaussians calculated via a
  classical kernel representation. Furthermore, we propose an unsupervised
  strategy to efficiently filter out anchors corresponding to static areas. Only
  anchors associated with deformable objects are input into MLPs to query
  time-variant attributes. Experiments on two real-world datasets demonstrate
  that our EDGS significantly improves the rendering speed with superior
  rendering quality compared to previous state-of-the-art methods.
paper_ai_summary: Efficient Dynamic Gaussian Splatting enhances rendering speed and quality by using sparse anchor-grid representation and unsupervised filtering for dynamic scenes.
paper_ai_keywords: deformable Gaussian Splatting Gaussians rendering speed sparse anchor-grid representation motion flow classical kernel representation unsupervised strategy MLPs time-variant attributes