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Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering (2401.15318v2)

Published 27 Jan 2024 in cs.GR, cs.AI, cs.CV, and cs.LG

Abstract: 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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Citations (2)
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Summary

  • The paper introduces a unified framework that integrates position-based dynamics with 3D Gaussian Splatting for realistic fluid-solid interactions.
  • It leverages Gaussian kernels with anisotropy regularization and inpainting to maintain rendering quality and simulate surface tension in dynamic fluids.
  • Experimental results demonstrate efficient dynamic scene synthesis, highlighting potential applications in VR, gaming, and visual effects.

Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting

The paper "Gaussian Splashing: Dynamic Fluid Synthesis with Gaussian Splatting" presents a comprehensive framework that integrates position-based dynamics (PBD) with 3D Gaussian Splatting (3DGS) to simulate and render interactions between solids and fluids. This work leverages the cohesive representation of these techniques to manage rendering, view synthesis, and dynamic simulations within a unified system.

Methodology

The authors employ a collection of Gaussian kernels to represent the scene's geometry, color, and dynamic properties. The framework facilitates interaction between solid objects and fluids using PBD. In the PBD framework, the dynamic system is represented as vertices and constraints, which are efficiently simulated through constraint projections. This integration enables realistic two-way coupling dynamics between solids and fluids, allowing for novel scene interactions.

3D Gaussian Splatting is exploited for the rasterization of these kernels, enabling high-quality rendering of dynamic scenes. Gaussian Shader further enhances this by incorporating material properties such as diffuse and specular components, providing accurate representation of reflective surfaces.

Technical Contributions

Several technical contributions are included in the paper:

  • Anisotropy Regularization: The authors introduce an anisotropy regularization term to prevent excessive elongation or compression of Gaussian kernels, maintaining rendering quality under large deformations.
  • Surface Tension in Fluids: Using position-based surface tension models, the framework captures fluid surface dynamics effectively. By estimating the surface normals of fluid particles, the framework synthesizes realistic surface reflections.
  • Inpainting for Texture Recovery: The occasional exposure of unseen areas during object displacement is tackled using generative AI for inpainting, ameliorating black smudges and rendering artifacts.

Experimental Results

The framework is validated through a range of experiments, demonstrating its capability to synthesize dynamic scenes with complex fluid-solid interactions. The experiments include scenarios where objects transform state from solid to fluid, enabling interesting effects such as indoor pools and flowing water.

Rendering results show that specular highlights greatly enhance realism, and anistropy regularization effectively reduces artifacts caused by large deformative movements. Evaluations reveal the system's efficiency in both simulation and rendering times.

Implications and Future Directions

The integration of PBD with 3DGS offers a scalable approach for dynamic scene synthesis, with potential applications in virtual reality, gaming, and visual effects. However, physiological accuracy in fluid dynamics remains a limitation, given PBD's inherent simplifications.

Future research may explore extending the framework to support more complex material properties, improve fluid rendering by incorporating physical models for refraction, and optimize performance for larger scenes with high fluid particle counts.

In conclusion, Gaussian Splashing exemplifies how unified representations can bridge simulation and rendering capabilities, offering a robust platform for exploring dynamic interactions in reconstructed 3D scenes. The framework sets a foundation for advancing physically-based rendering techniques, integrating machine learning with traditional graphics, and opening avenues for innovative interactive environments.

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