GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials
Bei Huang, Yixin Chen, Ruijie Lu, Gang Zeng, Hongbin Zha, Yuru Pei, Siyuan Huang

TL;DR
GaussianFluent is a novel framework that combines Gaussian-based volumetric interior synthesis with high-speed brittle fracture simulation, enabling realistic, real-time rendering of complex dynamic scenes with mixed materials.
Contribution
It introduces a unified approach for realistic interior synthesis and fracture-aware simulation of Gaussian representations, addressing key limitations of prior methods.
Findings
Enables photo-realistic interior rendering with coherent textures.
Supports high-speed brittle fracture simulation for complex objects.
Achieves real-time performance in dynamic scene rendering.
Abstract
3D Gaussian Splatting (3DGS) has emerged as a prominent 3D representation for high-fidelity and real-time rendering. Prior work has coupled physics simulation with Gaussians, but predominantly targets soft, deformable materials, leaving brittle fracture largely unresolved. This stems from two key obstacles: the lack of volumetric interiors with coherent textures in GS representation, and the absence of fracture-aware simulation methods for Gaussians. To address these challenges, we introduce GaussianFluent, a unified framework for realistic simulation and rendering of dynamic object states. First, it synthesizes photorealistic interiors by densifying internal Gaussians guided by generative models. Second, it integrates an optimized Continuum Damage Material Point Method (CD-MPM) to enable brittle fracture simulation at remarkably high speed. Our approach handles complex scenarios…
Peer Reviews
Decision·ICLR 2026 Conference Withdrawn Submission
- Fig. 2 provides a coherent end‑to‑end pipeline (interior filling → fracture‑aware simulation → relighting), and the text walks through each component with equations and ablations at a readable level - Identifying/discussing the tip‑projection discontinuity for the NACC return map and proposing a heuristic continuous projection is well motivated, and Fig. A1 effectively illustrates the issue; the write‑up is technically careful. - The spatially varying \beta examples (watermelon rind/flesh/s
- The abstract and intro emphasize “real‑time” or “real‑time speeds,” yet Table A1 reports 0.39–5.12 s/frame depending on scene, and App. B.3 states a reduction “from 4 minutes per frame to a single second,” i.e., ~1 fps—not real‑time interaction or real‑time rendering in the usual 24–60 fps sense - Evaluation is largely perceptual/hallucinatory, not physics‑grounded. - The internal textures are synthesized by 2D inpainting per slice and iterative refinements; while visually convincing (Fig. 6
1. The task is crucial for 3D computer vision. The 3DGS only focuses on the learning of surface appearances and ignore the inner structures, which further hinders the capability of physics modeling. 2. The framework is effective by introducing pretrained image models for appearance inpainting.
1. The method seems to ignore the lighting modeling of the full shape. The demo in teaser seems to only put the Gaussians into a preset scene without adding environment lighting, which looks wired. Since the method also focuses on a "lighting system", solving the environment lighting is also crucial. 2. How dose the rendering-to-inpainting framework keeps the consistency across the slice at different inner structures? For example, if the method is used to learn a physical 3DGS from a cake model
- The overall presentation is clear. - The internal texture inpainting is a good contribution to the PhysGaussian framework. The quality of the generated internal renderings is good. The comparison to previous method in terms of internal rendering is promising.
- There are many hardcoded parameters. No ablation studies are provided to justify these design choices. What artifacts may appear if these fixed numbers are too large or too small? Some examples: - In Iterative Texture Refinement, **5** steps SH optimizations are used for each Gaussian Optimization. - **40** slices are used to run inpainting. Is this number suitable for different sizes? - **0.1** strength are used in inpainting. - The shown reconstruction are limited to Objaverse
The authors propose a novel pipeline that provides internal structures and textures for 3DGS, which could support more photorealistic simulation. The reviewer finds this part interesting and novel. The authors introduce the CD-MPM algorithm into 3D-GS and implement the Blinn-Phong lighting model to the framework, supporting deformable simulation and dynamic lighting.
The proposed internal texture synthesis seems promising. While the reviewer is curious whether the physics information could also be assigned during the texture synthesis. The inpainting process mainly employs a 2D Diffusion inpainting model, which may cause multi-view inconsistency and affect geometry precision. Why not consider using a 3D diffusion inpainting model? e.g., Amodal3R (Wu et al., ICCV2025) Section 3.2 seems too heavy. Could the authors reorganize this section for better readabil
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
