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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
Novel View SynthesisNeu3D (test)
PSNR31.2
18
Novel View SynthesisPlenopticVideo (test)
PSNR31.31
9
Dynamic 3D ReconstructionHyperNeRF (test)
PSNR25.74
8
Dynamic Scene ReconstructionNeural 3D Video Dataset
PSNR32.35
8
Novel View SynthesisNeur3D
PSNR31.42
8
Novel View SynthesisPanoptic Sport basketball and boxes
PSNR25.61
7
Novel View SynthesisMPEG
PSNR29.94
6
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