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Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting

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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/

Jeongmin Bae, Seoha Kim, Youngsik Yun, Hahyun Lee, Gun Bang, Youngjung Uh• 2024

Related benchmarks

TaskDatasetResultRank
Dynamic 3D ReconstructionHyperNeRF (test)
PSNR25.74
18
Novel View SynthesisNeu3D (test)
PSNR31.2
18
Dynamic Scene ReconstructionN3V
Coffee Martini Score30.04
14
Monocular dynamic scene reconstructionHOSNeRF (test)
Backpack PSNR24.78
12
Dynamic Scene ReconstructionN3DV
PSNR30.79
9
Novel View SynthesisPlenopticVideo (test)
PSNR31.31
9
Dynamic Scene ReconstructionHyperNeRF vrig (test)
PSNR25.4
8
Dynamic Scene ReconstructionNeural 3D Video Dataset
PSNR32.35
8
Novel View SynthesisNeur3D
PSNR31.42
8
Dynamic Scene ReconstructionTechnicolor
Quality Score (Birthday Scene)32.38
7
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