Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian
  Splatting
Jeongmin Bae Seoha Kim Youngsik Yun Hahyun Lee Gun Bang Youngjung Uh
As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view
synthesis, it is a natural extension to deform a canonical 3DGS to multiple
frames. However, previous works fail to accurately reconstruct dynamic scenes,
especially 1) static parts moving along nearby dynamic parts, and 2) some
dynamic areas are blurry. We attribute the failure to the wrong design of the
deformation field, which is built as a coordinate-based function. This approach
is problematic because 3DGS is a mixture of multiple fields centered at the
Gaussians, not just a single coordinate-based framework. To resolve this
problem, we define the deformation as a function of per-Gaussian embeddings and
temporal embeddings. Moreover, we decompose deformations as coarse and fine
deformations to model slow and fast movements, respectively. Also, we introduce
an efficient training strategy for faster convergence and higher quality.
Project page: https://jeongminb.github.io/e-d3dgs/