SemDynReg: Semantics-Guided Deformation Regularization for Dynamic 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2606.28656v1
• Date: Sat, 27 Jun 2026 00:18:55 GMT
• Title: SemDynReg: Semantics-Guided Deformation Regularization for Dynamic 3D Gaussian Splatting
• Abstract summary: We introduce a semantics-guided framework that enforces dynamic regularization at the object level.<n>We achieve higher PSNR and SSIM and lower LPIPS in dynamic 3DGS rendering.
• Score: 1.1934335703226404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable 3D Gaussian Splatting (3DGS) has emerged as an efficient approach for rendering dynamic scenes in a wide range of 3D applications. However, existing deformation field-based approaches largely lack explicit object-level modeling, often resulting in inconsistent Gaussian deformations within individual objects and unwanted coupling between different objects. To address this limitation, we introduce a semantics-guided framework that enforces dynamic regularization at the object level, aiming to achieve spatially consistent object-wise deformation. Specifically, we first extract segmentation masks using the Segment Anything Model (SAM) and derive semantic features from input images. An object-ID map is then constructed via feature relevance matching with a predefined object dictionary. Guided by this object-ID map, we identify the pixel-wise top-k contributing Gaussians for each object and impose consistency regularization on their deformation parameters, including position, scale, and rotation. Unlike prior methods that learn deformation fields without explicit object-level constraints, our approach incorporates semantic cues to guide deformation behavior at the object level. Experimental results demonstrate that our semantics-aware regularization improves object-level deformation consistency and outperforms baseline methods in rendering quality, achieving higher PSNR and SSIM and lower LPIPS in dynamic 3DGS rendering. Our project page is available at https://dyn-reg-3dgs.github.io/.

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