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Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering

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arxiv 2401.15318 v2 pith:7ZFI4U2K submitted 2024-01-27 cs.GR cs.AIcs.CVcs.LG

Yutao Feng , Xiang Feng , Yintong Shang , Ying Jiang , Chang Yu , Zeshun Zong , Tianjia Shao , Hongzhi Wu
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Kun Zhou Chenfanfu Jiang Yin Yang
This is my paper · ORCID
classification cs.GRcs.AIcs.CVcs.LG
keywords fluidsgaussianrenderingsolidssurfacedynamicdynamicskernel
verification ladder T0 review T1 audit T2 compute T3 formal
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We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS. Leveraging the coherence of the Gaussian Splatting and Position-Based Dynamics (PBD) in the underlying representation, we manage rendering, view synthesis, and the dynamics of solids and fluids in a cohesive manner. Similar to GaussianShader, we enhance each Gaussian kernel with an added normal, aligning the kernel's orientation with the surface normal to refine the PBD simulation. This approach effectively eliminates spiky noises that arise from rotational deformation in solids. It also allows us to integrate physically based rendering to augment the dynamic surface reflections on fluids. Consequently, our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views. For more information, please visit our project page at \url{https://gaussiansplashing.github.io/}.

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Forward citations

Cited by 5 Pith papers

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

  1. FieryGS: In-the-Wild Fire Synthesis with Physics-Integrated Gaussian Splatting

    cs.GR 2026-04 unverdicted novelty 7.0 of 10

    FieryGS integrates LLM-based material reasoning, volumetric combustion simulation, and a unified renderer with 3D Gaussian Splatting to generate physically plausible and user-controllable fire in in-the-wild scenes.

  2. DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model

    eess.IV 2025-06 conditional novelty 6.0 of 10

    DIGS couples 4D Gaussian Splatting with a low-rank free-form deformation model to reconstruct motion-compensated CBCT images per projection, claiming better image quality and 6x faster training than HexPlane.

  3. DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    DualPM reduces monocular 3D shape and pose estimation of quadrupeds to predicting a pair of point maps (posed and canonical), and shows strong generalization from minimal synthetic training data.

  4. 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.

  5. Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives

    cs.CV 2024-11 reject novelty 6.0 of 10

    A Gaussian splatting method that fits curved paraboloid patches instead of flat disks reports better surface reconstruction, but its geodesic-distance justification is only exact for surfaces of revolution.

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