Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
  Sparse Time-Variant Attribute Modeling
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.
67c1aa781c3a8036977ed8b1
Hanyang Kong
claimed_verified
2025-02-28T15:13:50.949Z
6442882f8443bce4c98a88aa
/avatars/70d5aa651b07b43629554096d76efd4c.svg
Kong
user
imsuperkong
67c1aa781c3a8036977ed8b2
Xingyi Yang
admin_assigned
2025-02-28T12:45:42.180Z
634cfebc350bcee9bed20a4d
https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
Xingyi Yang
user
adamdad
67c1aa781c3a8036977ed8b3
Xinchao Wang
admin_assigned
2025-02-28T12:45:56.197Z
63fc03a50aab060792ffef39
/avatars/9d5b1bb2a41928e08176b703935133ab.svg
Wangxinchao
user
wxcTest
Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
  Sparse Time-Variant Attribute Modeling
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.
Efficient Dynamic Gaussian Splatting enhances rendering speed and quality by using sparse anchor-grid representation and unsupervised filtering for dynamic scenes.
deformable Gaussian Splatting Gaussians rendering speed sparse anchor-grid representation motion flow classical kernel representation unsupervised strategy MLPs time-variant attributes