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SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes

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arxiv 2312.14937 v3 pith:WJ7ZI6ZQ submitted 2023-12-04 cs.CV cs.GR

Yi-Hua Huang , Yang-Tian Sun , Ziyi Yang , Xiaoyang Lyu , Yan-Pei Cao , Xiaojuan Qi This is my paper · ORCID
classification cs.CVcs.GR
keywords motioncontrolscenesgaussiansnovelpointsappearancedynamic
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Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point, which reduces learning complexities, enhances learning abilities, and facilitates obtaining temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians, the canonical space locations of control points, and the deformation MLP to reconstruct the appearance, geometry, and dynamics of 3D scenes. During learning, the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions, and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally, thanks to the explicit sparse motion representation and its decomposition from appearance, our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications. Project page: https://yihua7.github.io/SC-GS-web/

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A pose-free two-image pipeline decomposes a dynamic scene into rigid objects and fits per-Gaussian SE(3) motions to synthesize novel views of moving scenes.

  2. Spline Deformation Field

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Cubic Hermite spline trajectories with analytic velocity and acceleration regularizers improve sparse-frame interpolation and spatial coherence in dynamic scene reconstruction.

  3. HuSc3D: Human Sculpture dataset for 3D object reconstruction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    HuSc3D provides six real-world scenes of white, low-texture sculptures with varied capture conditions, and benchmarks show Gaussian-splatting methods clearly outperform NeRF-based methods.

  4. 4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes

    cs.CV 2024-12 conditional novelty 6.0 of 10

    SaRO-GS models dynamic scenes with 4D Gaussians plus a scale-aware residual field and adaptive per-Gaussian optimization, achieving state-of-the-art PSNR at real-time frame rates on D-NeRF and Plenoptic Video datasets.

  5. Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A comprehensive benchmark shows monocular dynamic Gaussian splatting methods are fast and brittle, with scene complexity dominating method differences.

  6. GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction

    cs.CV 2024-11 conditional novelty 6.0 of 10

    GausSurf combines iterative patch-match MVS refinement with 3D Gaussian Splatting and normal priors, achieving state-of-the-art surface reconstruction at 7.2 minutes per DTU scene.

  7. Neural Surface Priors for Editable Gaussian Splatting

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A neural-surface-guided Gaussian splatting pipeline propagates mesh edits through a triangle soup proxy, enabling mesh-based editing of reconstructed scenes.

  8. Leveraging 2D Priors and SDF Guidance for Dynamic Urban Scene Rendering

    cs.CV 2025-10 conditional novelty 5.0 of 10

    UGSDF achieves state-of-the-art novel-view rendering of dynamic urban objects without LiDAR or 3D motion annotations by jointly optimizing SDFs and 3D Gaussians under 2D depth and point-tracking priors.

  9. Gaussians on their Way: Wasserstein-Constrained 4D Gaussian Splatting with State-Space Modeling

    cs.CV 2024-11 reject novelty 5.0 of 10

    A 4D Gaussian splatting method that adds a Kalman-like state consistency filter and Wasserstein log/exp geometry reports quality gains on dynamic scene benchmarks, but internal equation errors and unverified training-...

  10. Exploring Dynamic Novel View Synthesis Technologies for Cinematography

    cs.CV 2024-12 unverdicted novelty 2.0 of 10

    A review of dynamic novel view synthesis for cinematography, accompanied by a self-made montage using Nerfacto, 4D-GS, and SC-GS.

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