💻 computer science

GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials

GaussianFluent is a unified framework that enables realistic, real-time rendering and simulation of dynamic objects with mixed materials and brittle fracture by synthesizing photorealistic volumetric interiors via generative models and integrating an optimized Continuum Damage Material Point Method.

Original authors: Bei Huang, Yixin Chen, Ruijie Lu, Gang Zeng, Hongbin Zha, Yuru Pei, Siyuan Huang

Published 2026-01-15

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CC BY 4.0

Original authors: Bei Huang, Yixin Chen, Ruijie Lu, Gang Zeng, Hongbin Zha, Yuru Pei, Siyuan Huang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you have a digital sculpture made of thousands of tiny, glowing fog balls (called "3D Gaussians"). These fog balls are great at painting a picture of what something looks like from the outside, but they are hollow inside. If you try to smash a digital watermelon made of these fog balls, the inside just vanishes into empty space, or worse, it looks like a blurry mess because the computer doesn't know what the red flesh or black seeds look like.

GaussianFluent is a new tool that fixes this. It turns those hollow fog sculptures into solid, realistic objects that can be smashed, sliced, and squished in a way that looks exactly like real life. Here is how it works, broken down into two main magic tricks:

1. Filling the Hollow Shell (The "Ghost Chef" Trick)

The Problem: Standard 3D fog balls only know the skin of an object. They don't know what's inside. If you cut a cake, the computer has no idea if there are layers of sponge and cream or just more cake.

The Solution: GaussianFluent acts like a "Ghost Chef."

Step 1: It first fills the empty space inside the object with more tiny fog balls, creating a solid volume.
Step 2: It then uses a "smart painter" (an AI image generator) to look at the object from the outside and imagine what the inside should look like. It paints the inside of the fog balls with realistic textures—like the red juice of a watermelon or the blue sugar inside a jelly.
The Result: Now, when you slice the object, the computer doesn't just show a hole; it reveals a brand-new, realistic interior that matches the outside perfectly.

2. Making Things Break and Squish (The "Toy Box" Trick)

The Problem: Previous methods could make objects wiggle or stretch like rubber bands (elastic), but they couldn't handle things that shatter or break apart (brittle). If you shot a bullet through a jelly, the old methods would just make the jelly stretch weirdly or fail to break. Also, they couldn't handle "mixed materials"—like a lollipop where the candy shatters but the wooden stick stays whole.

The Solution: GaussianFluent uses a special physics engine called CD-MPM (Continuum Damage Material Point Method). Think of this as a super-advanced toy box where every single fog ball has a personality and rules:

Different Personalities: You can tell the computer, "The green skin of the watermelon is tough, the red flesh is soft, and the black seeds are hard."
The Breaking Rule: It uses a mathematical rule to decide when something is "stressed" enough to break. Instead of just snapping at a specific point, it slowly weakens the material as it gets squished or stretched until it finally gives way.
The Bullet Test: Because it understands these rules, it can simulate a bullet flying through a jelly, pushing the sugar out the other side, or a watermelon falling and cracking open to reveal the seeds, all while the wooden stick of a lollipop remains unbroken.

Why This Matters (According to the Paper)

The paper claims this is a big leap forward because:

It's Fast: It runs on powerful computer chips (GPUs) so you can see these explosions and slices happen in real-time, not after waiting hours.
It's Realistic: It handles "mixed materials" better than anyone else. You can have a fruit with a hard shell and soft inside, and they will break differently.
It's Consistent: The inside texture looks the same no matter which angle you look at it from, even while the object is spinning or breaking apart.

In short: GaussianFluent takes a hollow, static 3D model and turns it into a living, breathing digital world where objects have solid insides, realistic textures, and the ability to break, slice, and splash just like they would in the real world. The authors tested this on things like watermelons, cakes, jelly, and milk, showing that it can handle everything from soft squishing to hard shattering.

Technical Summary: GaussianFluent

Problem Statement

While 3D Gaussian Splatting (3DGS) has established itself as a state-of-the-art technique for high-fidelity, real-time rendering of static scenes, its application to dynamic scenes involving complex physical interactions remains limited. Existing methods coupling physics simulation with Gaussians (e.g., PhysGaussian) primarily focus on elastic deformations of soft materials. They face two critical unresolved challenges when simulating brittle fracture and mixed-material dynamics:

Lack of Volumetric Interiors: Standard 3DGS is a surface-based representation. It inherently lacks defined internal structures, making it impossible to simulate or render newly exposed surfaces during events like fracture, slicing, or impact. Current reconstruction methods leave interiors hollow and textureless.
Absence of Fracture-Aware Simulation: Existing GS simulation methods are confined to elastic dynamics. Point-cloud fracture methods are incompatible with GS due to a lack of continuous return-mapping schemes (leading to physically implausible dynamics) and severe performance bottlenecks from CPU-bound execution. Furthermore, they fail to model mixed-material objects (e.g., a watermelon with a hard rind, soft flesh, and seeds) or simulate brittle fracture and topological changes within the GS framework.

Methodology: GaussianFluent

The authors propose GaussianFluent, a unified framework designed to synthesize realistic internal textures and simulate complex object dynamics, including brittle fracture and mixed-material interactions, within the 3DGS representation. The framework consists of two primary components:

1. Internal Texture Synthesis

To address the lack of internal structure, GaussianFluent introduces a pipeline to populate and texture the interior volume of Gaussians without requiring additional training data:

Internal Volume Initialization: The method first trains a standard 3DGS model augmented with a scale regularization loss. This encourages smaller, localized Gaussians to ensure a clear separation between the exterior surface and the interior volume. The object boundary is identified via a density field, and new Gaussians are initialized within the enclosed volume. An opacity-only optimization refines this volume, pruning extraneous Gaussians to create a clean solid representation.
Two-Stage Texture Generation:
- Coarse Initialization: The object is sliced along the X-axis. A generative model (MVInpainter) performs single-view inpainting on these slices guided by text prompts and reference images (generated via Stable Diffusion XL) to create a consistent initial texture.
- Iterative Refinement: To ensure 3D consistency across viewing directions, the method employs an iterative refinement loop across all three axes (X, Y, Z). Using low-strength inpainting updates guided by SD-XL, the system incrementally injects internal details while maintaining global structure. The spherical harmonic (SH) coefficients of the internal Gaussians are optimized to minimize rendering discrepancies against these inpainted slices, achieving tri-axial consistency.

2. Optimized CD-MPM for Mixed Materials

The framework integrates an optimized Continuum Damage Material Point Method (CD-MPM) to enable high-speed, physically accurate simulation of brittle fracture and mixed materials:

Physics Integration: Each 3D Gaussian is treated as a particle carrying physical properties (mass, velocity, stress, etc.) and interacts via a background Eulerian grid. The deformation gradient ( $F_p$ ) is applied to the Gaussian's covariance and spherical harmonics to update its state.
Fracture Mechanism: The method models brittle fracture using a Non-Associated Cam-Clay (NACC) yield surface. A softening law reduces the material's stress-generating capacity as deformation accumulates. Fracture occurs when this capacity vanishes, tracked by a damage variable $\alpha$ .
Continuous Return Mapping: To resolve numerical instabilities in previous CD-MPM implementations (specifically discontinuities at the yield surface tips), the authors introduce a regularized continuous return mapping. This replaces the fixed projection center with a dynamic point that adapts smoothly to the stress state, ensuring stable and physically plausible fracture dynamics.
Mixed Material Support: Unlike prior works assuming uniform properties, GaussianFluent assigns distinct material parameters (specifically the damage parameter $\beta$ ) to different parts of an object (e.g., seeds, flesh, rind of a watermelon) based on segmentation or heuristics.
Unified Solid-Fluid Simulation: By adjusting material parameters (specifically setting the pre-consolidation pressure $p_0 \to 0$ ), the NACC model naturally degenerates to fluid behavior, allowing the same framework to simulate both brittle solids and fluids (e.g., milk splashing) without separate solvers.
GPU Parallelization: The entire simulation loop is re-implemented on the GPU using the NVIDIA Warp library, achieving real-time performance (seconds per frame) compared to minutes for CPU-based methods.

Key Contributions

Internal Texture Synthesis: A novel, training-free pipeline that synthesizes photorealistic, spatially consistent internal structures and textures for 3DGS using generative models, enabling the visualization of fracture interiors.
Optimized CD-MPM for GS: An integration of CD-MPM with 3DGS that resolves previous instability issues via a continuous return mapping scheme and implements GPU parallelism for high-speed simulation.
Mixed-Material and Fluid Simulation: The ability to simulate complex scenarios involving mixed materials (e.g., rigid bullets penetrating jelly, watermelons fracturing) and fluid-like behaviors within a single unified framework.
Dynamic Relighting: The integration of a Blinn-Phong shading model with PCA-based normal computation, allowing for realistic rendering under dynamic lighting conditions and evolving shadows.

Results

The authors validate GaussianFluent on a suite of challenging scenarios involving food, liquids, and fruits with diverse material properties (brittle solids, viscoelastic gels, soft tissues).

Texture Quality: Quantitative (CLIP scores) and qualitative user studies demonstrate that GaussianFluent significantly outperforms baseline methods (PhysGaussian and 2D Inpainting) in generating realistic, consistent, and detailed internal textures.
Simulation Fidelity: The method successfully simulates diverse topological changes, including:
- Brittle Fracture: Watermelons falling and shattering, revealing seeds and flesh; lollipops shattering while sticks remain intact.
- Mixed-Material Dynamics: Rigid bullets penetrating jelly and expelling internal sugar; slicing through layered cakes.
- Fluid Dynamics: Milk splashing on a table and sandcastles collapsing under gravity.
Performance: The GPU-optimized implementation achieves real-time rendering speeds (e.g., ~0.39s/frame for jelly, ~3.56s/frame for complex watermelon scenes), a substantial improvement over previous CPU-bound approaches.

Significance and Claims

The paper claims that GaussianFluent represents a significant step forward in bridging the gap between high-fidelity rendering and physically accurate simulation. By solving the problems of undefined interiors and brittle fracture, the framework enables the realistic simulation of dynamic events that were previously infeasible in the GS domain. The authors highlight the potential for downstream applications in Virtual Reality (VR) and Robotics, where interactive, physically plausible, and visually rich dynamic environments are essential. The work demonstrates that 3DGS can now handle complex structural changes and mixed-material interactions with high visual fidelity and structural consistency.