Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting
About
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 for representing a dynamic scene. However, previous works fail to accurately reconstruct complex dynamic scenes. We attribute the failure to the 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 a local smoothness regularization for per-Gaussian embedding to improve the details in dynamic regions. Project page: https://jeongminb.github.io/e-d3dgs/
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Novel View Synthesis | Neu3D (test) | PSNR31.2 | 18 | |
| Novel View Synthesis | PlenopticVideo (test) | PSNR31.31 | 9 | |
| Dynamic 3D Reconstruction | HyperNeRF (test) | PSNR25.74 | 8 | |
| Dynamic Scene Reconstruction | Neural 3D Video Dataset | PSNR32.35 | 8 | |
| Novel View Synthesis | Neur3D | PSNR31.42 | 8 | |
| Novel View Synthesis | Panoptic Sport basketball and boxes | PSNR25.61 | 7 | |
| Novel View Synthesis | MPEG | PSNR29.94 | 6 |