Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via
  Sparse Time-Variant Attribute Modeling
Authors: name: Hanyang Kong, name: Xingyi Yang, name: Xinchao Wang

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.