FieryGS: In-the-Wild Fire Synthesis with Physics-Integrated Gaussian Splatting
Authors: Qianfan Shen
Ningxiao Tao
Qiyu Dai
Tianle Chen
Minghan Qin
Yongjie Zhang
Mengyu Chu
Wenzheng Chen
Baoquan Chen
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian Splatting
Physics Simulation
Combustion Simulation
Novel View Synthesis
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: We consider the problem of synthesizing photorealistic, physically plausible combustion effects in in-the-wild 3D scenes. Traditional CFD and graphics pipelines can produce realistic fire effects but rely on handcrafted geometry, expert-tuned parameters, and labor-intensive workflows, limiting their scalability to the real world. Recent scene modeling advances like 3D Gaussian Splatting (3DGS) enable high-fidelity real-world scene reconstruction, yet lack physical grounding for combustion. To bridge this gap, we propose FieryGS, a physically-based framework that integrates physically-accurate and user-controllable combustion simulation and rendering within the 3DGS pipeline, enabling realistic fire synthesis for real scenes. Our approach tightly couples three key modules: (1) multimodal large-language-model-based physical material reasoning, (2) efficient volumetric combustion simulation, and (3) a unified renderer for fire and 3DGS. By unifying reconstruction, physical reasoning, simulation, and rendering, FieryGS removes manual tuning and automatically generates realistic, controllable fire dynamics consistent with scene geometry and materials. Our framework supports complex combustion phenomena—including flame propagation, smoke dispersion, and surface carbonization—with precise user control over fire intensity, airflow, ignition location and other combustion parameters. Evaluated on diverse indoor and outdoor scenes, FieryGS outperforms all comparative baselines in visual realism, physical fidelity, and controllability.

Published as a conference paper at ICLR 2026
FIERYGS: IN-THE-WILD FIRE SYNTHESIS WITH PHYSICS-INTEGRATI GAUSSIAN SPLATTING
Qianfan Shen', Ningxiao Tao Qiyu Dai Tianle Chen', Minghan Qin', Yongjie Zhang',
Mengyu Chu?; Wenzheng Chen : Baoquan Chen
Schooo of EECS, Peking University
School of Intelligence Science and Technology, Peking University
3 Yuanpei College, Peking University
*ByteDance Seed
WWangxuan Institute of Computer Technology, Peking University 'Bijiig Academy of Artificial Intelligence, Beijing, China
ABSTRACT
We consider the problem of synthesizing photorealistic, physically plausible com- bustion effects in in-the-wild 3D scenes. Traditional CFD and graphics pipelines can produce realistic fire effects but rely on handcrafted geometry, expert-tuned parameters, and labor-intensive workflows, limiting their scalability to the real world. Recent scene modeling advances like 3D Gaussian Splatting (3DGS) en- able high-fidehi figh-fidelity real-world scene reconstruction, yet lack physical grounding for combustion. To bridge this gap, we propose FieryGS, a physically- framework that integrates physically-accurate and user-controllable combustion simulation and rendering within the 3DGS pipeline, enabling realistic fire synthesis for real scenes. Our approach tightly couples three key modules: (1) multimodal mere--aanngge-meel--beeeeee
physical material reasoning, (2) efficient volumetric combustion simulation, and (3) a unified renderer for fire and 3DGS. By uni- fying reconstruction, physical reasoning, simulation, and rendering, FieryGS removes manual tuning and automatically generates realistic, controllable fire dynamics consistent with scene geometry and materials. Our framework supports complex combustion ineenomeaa-incluee sion, and surface carbonization-wittl precise user control over fire intensity, airflow, ignition location and other combustion parameters. Evaluated on diverse indoor and outdoor scenes, FieryGS outperforms all comparative baselines in vi- sual realism, physical fidelity, and controllability. Project page can be found at https: httes//ppkuu //pk///kk/////////// -geomet ry github. io/FieryGS/
flame propagation, smoke disper-
Multi-view Inputs
Increasing time steps rotating viewpoints
Figure 1: FieryGS synthesizes physically- fire effects from multi-view image. enabling controllable and realistic fire in-the-wild scenes.
Joint first authors PProjcct lead
Coorrisnnning authors

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INTRODUCTION
Synthesizing realistic and controllable combustion effects grounded in-the-wild 3D scenes is critical for applications ranging from AR/VR, gaming, and film production to virtual fire drills, heritage preservation, and robotics perception under adverse conditions, where fire must be visually convincing, physically plausible, interactively controllable, and well-aligned with the real world. Existing approaches, however, fall short of meeting these requirements (Table
Figure 2: Left: Real-world combustion in live-fire drill (5280 Fire Science); Right:Full-scale combustion test measuring flame spread time (Zhang et al. 202T)
Table 1:
Applicability comparison combustion approaches. FieryGS offers accessible fire simulation for real-world scenes by combining scene-aligned physics, visual fidelity, efficiency, and user control.
Method
Real-world Applicability
Visual Fidelity
Physical Fidelity
Parametor
Priendliness
Scalability
Full-scale Experiments
CFD Methods
sim real gap sim-to-real gap
expert-only expert-only
VFX Tools
Commercial Software
pre stored
Large Video Models
X
FieryGS (Ours)
Notes: Real-world Applicability indicates case of use real scenes, where CHD/VFX requires manual modeling. Visual Fidelity measures perceptual realism, where CTD/VTX saffer sim real gaps and commercial software overlays pre computed results. Physical Fidelity checks consistency with physics, where large video medels are data-driven and commercial software uses pre-stored effects. Parameter Control rellects ability vary conditions,
scale experiments are cosily to repeat, large video commercial software is limited to pre-stored effects. User Friendliness considers usahility, where full-scale experiments are dangerous and CFD/VEX requires experts. Sealability is automatic adaptation to new scenes at low cost. Full-seale experiments are expensive, CFD/VFX needs manual modeling. and commercial software limited pre-stored effects.
precise control,
The most authentic option-full
fire experiments, such as burning life-sized structures (Fig. i is prohibitively expensive, risky, and irreprodueible, making systematic exploration under varying conditions infeasible. A Alternatively, digital approaches like computational fluid dynamics (CFD) or visual effect (VFX) software (c.g., Houdini, Blender) incorporate physics-based simulation but depend on asset construction, detailed material annotation, carefully discretized geometry, and brittle -eendiiinn-reerinng pipelines (Lakkonen) 2024 Mahadika & Utami 2025). Thus, targeting real-world scenes demands impractical manual specification, and each step remains incomplete approximation, inevitably producing a pronounced sim-to-real gap that limits practical deployment. With the rise of Large Video Models (LVM),, it has become possible add fire effects directly to footage, but the results lack physical consistency and precise controllability. Due to these limita- tions, current commercial software (SimsUshare) 2025) Digital Combustion 2025 instead relies on overlaying pre-stored fire effects onto scenes, without ensuring physical fidelity.
Recent advances in scene modeling present new opportunities. Methods such as Neural Radiance Fields (NeRF) (Mildenhll et al., (020) and 3DGS (errl et al. 2023) enable high fighlfideiity 3D re- construction from multi-view images. providing highly detailed surface information with strong real-world alignment. Although primarily designed for static appearance capture, their visual fidelity and rendering efficiency suggest potential for further material inference and physics-infeee informed mod- cling. Some prior works leverage such reconstructions to incorporate physical properties (Li al.) Cai et al. 2024: Li et al. 2023] Feng et al.. 2024 Dai et al. 2025; Hsu et al. (2024) to model related phenomena such fluid dynamies deformable objects. However, realistic combustion remains out of reach, as it requires accurate scene- material inference, complex simulation tightly coupled with scene representation, and fine- fine-grained controllability over fire behavior.
To bridge this gap. we introduce FieryGS, a physically based framework that integrates accurate and controllable combustion simulation into the 3DGS pipeline. Our method automatically generates
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photorealistic, dynamic fire in reconstructed scenes while allowing precise control over fire intensity, airflow, ignition location, and other parameters. The framework tightly couples three components:
material reasoning, zero-shot inferring combustion-relevant reliable properties from 3DGS reconstructions;
Controllable volumetric combustion simulation with wood charring via principled balance of computational cost and visual realism;
novel unified renderer, combining fire, smoke, and 3DGS for seamless photorealistic emission and illumination.
Tightly coupling these modules enables realistic fire effects to emerge directly from real-world data without expert design or handcrafted inputs. FieryGS is, to our knowledge, the first framework that generates visually and physically realistic combustion in in-the-wild scenes, while being efficient and supporting precise user controls over ignition location, fire intensity, airflow, and other parameters. Experiments across tabletop, indoor, and outdoor scenarios show that FieryGS outperforms state-of- the-art baselines in visual realism, physical fidelity, and user controllability, advancing fire synthesis from labor-intensive, expert-heavy workflows automatic, real-world aligned process.
RELATED WORK
Challenges in Combustion Simulation Combustion simulation has long been studied in both CFD and computer graphics. with physically based models developed to replicate fire behavior (Husain
Srivastava, 2018: Nguyen al.) 2002] Nielsen et al.] 2022 Feldman et al. (2003 Kwatra et al. 2010), material changes such as pyrolysis and charring of wood (Liu et al. 2024a). and volumetric rendering of flames and smoke (Huang et 2014 Nguyen et al.) 2002] Pegoraro Parker 2006). While these methods excel in specific aspects, they rely heavily
manual inputs, such as detailed geometry and material properties, and often require expert knowledge to combine multiple tools, resulting in limited flexibility and sim-to-real gaps in diversity and fidelity. Existing commercial software (Sims Ushare, 2025 Digital Combustion 2025) supports sall-wrldd case studies but relies on pre-stored fire effects, lacking both physical consistency and control over fire parameters. These limitations motivate a combustion framework that can automatically align with real-world scenes while maintaining efficiency, controllability, and physical accuracy.
Neural Scene Representations for Thysically-Grounde Editing Recent NeRF and 3DGS repre- sentations have enabled high-fidelity 3D reconstruction and inspired extensions to physical property inference. Some estimate parameters like Young's 's modulus, fluid viscosity, friction or stiffness from
videos (Li et al. Cai et al. 2024 Cao et al. 2024; Zhong et al.) 2024), while others (Zhang et al.) 2024 Huang et al. 2024a Liu et al. 2024b Lin et al. 2025 Liu et al. 2025) exploit dynamics in video models to infer material properties. LLMs provide a complementary direction to physical property reasoning, in NeRF2Physics (Zhai et al. 2024), GaussianProperty (Xu et al.; (2024), and PUGS (Shuai et al. 2025). However, they remain object-centric and do not address combustion- related attributes. Parallel efforts integrate explicit simulation with neural representations, including deformable bodies via Material Point Method (MPM) (Xie et al. (0244] Zhang et al. 22024) Huang
al. 2024a Liu al. 2024b), weather phenomena (Li al. [2023). fluid-solid interactions (Feng
et al. 2024), and rainfall (Dai et al.) 2025). Auto (Hsu et al., 2024) supports flame effects using Blender's built-in physics, but its dynamics are driven by LLM L.M--neerated scripts rather than spatiotemporal physical interactions, lacking physical consistency and control. We address this gap by introducing the first framework that integrates combustion simulation with 3DGS, enabling controllable and physically faithful fire synthesis.
3 METHODS
Given multi-view images, we reconstruct 3DGS scenes and infer combustion properties through zero-shot MLLM reasoning (Sec. B.1). The properties guide a physics-based combustion simulation (Sec. 3.2), which is rendered together with the scene using unified volumetric rendering (Sec. (33)
Fig. illuutrates the pipeline.
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Scene Modeling
User Control
Synthesized Fire Output
Multi-View Input
Fire interityy Aittion Igakion location
Combustion Simulation
Fire Simulation
Charring Simulation
Dupih Kap
Norma
Combustion Property Reasoning
Combustion Rendering
Materfal Ressoning
Burnabiity Reasoning
Figure 3: Overall Pipeline of FieryGS. Given multi-view images input, we first apply PGSR (Chen 2024) to reconstruct scenes with high-quality normal and depth. Next, we leverage MILM infer combustion- related properties, such as material type and burnability. Based on these, we conduct combustion simulations, enabling fire and charring effects with user control. A unified volumetric renderer seamlessly integrates 3DGS and fire, accounting for smoke scattering, fire illumination, and charring, producing realistic fire results.
3.1 SCENE MODELING WITH COMBUSTION PROPERTY REASONING
High-fidelity 3D modeling of appearance, geometry, and physical properties in in-the-wild scenes is essential for realistic combustion simulation. We adopt PGSR (Chen et al., 2024), recent 3DGS- based method that jointly reconstructs photorealistic appearance and accurate geometry, for scene reconstruction. To enable physically plausible fire simulation, we estimate combustion-relevan material properties for each Gaussian in reconstructed 3DGS, including material type, burnability, thermal diffusivity, and smoke color. Recent MLLMs have shown strong capabilities in inferring material from 2D images. However, extending the capabilities to in-the-wild 3DGS scenes remains challenging. Intuitively, nearby Gaussians with visual similarities are likely share same material properties. Inspired by recent work in 3DGS segmentation (Ye et al.) 2024; (en et al.) 2025), ) we first partition Gaussians into coherent 3D regions, each with shared material. Then, each region is rendered to 2D and passed to an MILMM for material inference. To ensure reliable MI.LM prediction, inference is performed from the viewpoint where the larget 3D region has the highest visibility.
Preliminary of 3D Gaussian Splatting 3DGS (Kerbl et al. 2023) models a scene as a set of anisotropic Gaussians, each parameterized by its center, covariance (E), opacity, and view-dependent color encoded with spherical harmonics. During rendering, a Gaussian is projected into screen space with covariance Σ' JV WV J where V is the camera extrinsic matrix and J the Jacobian of the projection. Pixel colors are obtained by alpha blending over depth-sorted Gaussians.
3D Gaussian Segmentation Given reconstructed 3DGS model, we first assign each Gaussian learnable feature vector fg E RR,,, where D the feature dimension. These features are rendered into 2D feature maps via 3DGS alpha blending. We then apply SAM (Kirillov et al. 2023). foundation model for 2D segmentation, to obtain segmentation maps across multiple views. Following SAGA (Cen et al. 2025). we adopt contrastive learning to train the feature vectors fg. encouraging pixels within the same mask to share similar embeddings. After training, Gaussians associated with the same 3D region exhibit similar features. We then apply HDBSCAN algorithm (MeInnes et al.) 2017) to cluster these feature vectors into instance-level 3DD segments, each assumed to correspond to a distinct material region (See Appendix A.I.I for hyperparameter details).
MLLM-based Combustion Property Reasoning For each segmented region in 3D Gaussians, we rasterize it into 2D and perform material inference using an MILMM In real-world scenes, complex occlusions cause large visibility differences across viewpoints, and limited exposure to the target region can degrade MLLM prediction accuracy. To address this, we select the viewpoint where the target 3D region has the highest visibility, determined by counting the number of unoccluded Gaussians based on rendered depth maps. We then feed GPT-4o (Hurst et al., 2024) a set of 2D renderings from the selected viewpoint, along with tailored prompt (see Appendix 1.22) to infer the material type and combustion- relevvan physical properties. The predicted attributes are projected back to 3D by directly assigning all Gaussians in the corresponding region. On average, GPT-4o
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store
cerniic
metal
RGB image input
Materials Gaussians
burnable
(c) Fire and smoke syothesized by FleryGS
(d) Burability of 3D Gaussians
Figure 4: Combustion Property Reasoning, Given an RGB input (a), our method reliably predicts material lypes (b) and burnabiiity (d). In complex region with metal spoons inside mug surrounded by various materials, method distinguishes the spoons and correctly infers their non-flammable metallic nature. These results drive the combustion simulation and rendering. where material-specific behaviors are applied for instance, combustion produces white smoke for the wooden box and black smoke for the plastic Lego (c).
API calls cost about $0.55 per scene, making our pipeline highly economical (see Appendix B 2 further validate the robustness and accuracy of the material reasoning results (see Appendix B.3
The result is 3DGS augmented with physical and combustion-awarr attributes (Fig. An occupancy grid is then constructed, where a voxel is labeled as occupied if it overlaps with one or more 3D Gaussians whose opacity exceeds given threshold, and further labeled combustible if any of these Gaussians are burnable. This grid defines the domain for combustion simulation, with unoccupied voxels representing air regions and occupied voxels representing solid regions.
3.2 COMBUSTION SIMULATION
Given the occupancy grid obtained in Section 3.1 we run combustion simulation in two parts. Fire simulation computes combustion state in air regions, which are then used for flame and smoke ren- dering. Charring simulation updates combustible regions with the degree of charring, supporting the rendering of charred surfaces. Focusing on efficiency, our method simplifies processes that have little visual impact, striking balance between computational performance and visual fidelity. Compared to CFD and VFX methods, which require manual geometry modeling and explicit specification of combustible regions, our pipeline leverages scene modeling and material reasoning to automatically initialize geometry, infer material properties, and identify combustible areas. Meanwhile, users retain flexible control over key parameters, making the simulation workflow largely automated and easy to customize. the following, we present fire simulation, charring simulation, and user control, while further implementation details are provided in Appendix A.2
Fire Simulation
We model flame dynamics using the following equations:
du 0t
- s.t. V u= at
OY
+u
= Vu= P
+ = =
(I)
where is the divergence-free velocity field, and p denote density and pressure, and Y is the reaction coordinate variable (Y for burning material, Y for unburnt material). At the beginning of the simulation, all voxels are initialized with Y = 0. Only the voxels corresponding to user-specified ignition points, which are also predicted as combustible in the occupancy grid, are set to Y 1, indicating the onset of combustion.
In this formulation, we choose an incompressible flow model (Nguyen et al. 2002) to balance physical plausibility with computational simplicity, in contrast to compressible formulations (Liu et al. 2024a) that provide higher physical fidelity but at the cost of greater complexity. Among the consider buoyancy force fouo a(T -Tair )z and vorticity confinement force foor (Nguyen et al. 2002). To further improve efficiency, the temperature is approximated as quadratic function of reaction coordinate variable Y, rather than solved through PDE-based thermal
external forces in Eq.
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models (guuyen al. 2002 Nielsen et al. 2022). This simplification makes the simulation pipeline more concise while still capturing the correlation between combustion progress and temperature.
Charring Simulation For combustible solids, we simulate temperature evolution by solving simplified heat transfer equation:
ÖTm TV + Ymm (T Tmh) STm 01
(2)
where Tm denotes the material temperature, 33 is the thermal diffusivity, and 7m is the radiative cooling coefficient. To avoid the high cost of explicitly modeling internal heat generation, ST,,, is approximated by clamping Tm to Thurn once the ignition threshold Tign is exceeded. Based on the simulated temperature, the relative char mass is computed aM. =T where Mc denotes the relative char mass, with M, = 1 representing a fully charred state and M, = 0 indicating the opposite. The parameter &, represents the charring rate, while E(Tm) equals 1 if Tm Tign and 0 otherwise. Unlike prior work that incorporates more detailed mechanisms such as insulation-layer formation or volatile release (Liu al. 2024 our formulation deliberately omits these processes. This simplification makes the simulation more efficient while still capturing the visually dominant aspects of charring. Subsequently, each 3D Gaussians directly inherits the Mc value from its containing grid voxel, providing a simple mapping to guide charring visualization.
User Control Our combustion simulation framework provides users with high degree of con- trol over key aspects of the simulation, including ignition location, fire intensity, and airflow, as demonstrated in Fig. 7 Specifically, users can accurately set the ignition point by assigning reaction coordinate variable = 1 to the target ignition voxel. The perceived fire intensity can be adjusted by increasing the buoyancy force coefficient a, which lifts the flames higher, and decreasing the reaction rate kt which extends flame visibility- both contributing to a visually stronger fire effect. Airflow can be flexibly controlled by adding an external wind force, enabling users to steer the fire as desired. In addition to these core controls, all other combustion parameters such as thermal diffusivity B, charring rate & are also accessible, allowing users to fine-tune the simulation for customized effects.
3.3 COMBUSTION RENDERING
We introduce the first rendering fraamwork that jointly integrates simulated fire, smoke, and recon- structed 3DGS into a unified volumetric pipeline. It builds upon the reconstructed 3DGS and the grids obtained from Section 3.2, including the reaction coordinate variable Y for fire and smoke and the relative char mass M. for charring. Using this information, the framework generates the final rendered image that seamlessly combines combustion effects with scene geometry.
Our framework builds upon volumetric rendering (Fong et al. 2017) with targeted simplifications tailored to combustion. Since fire is modeled as blackbody radiator with negligible scattering. and smoke is treated as a low-albedo medium, we omit scattering terms (Nguyen et al. [002] Pegoraro & Parker 2006). The 3DGS rendered as an opaque background where charring effects are incorporated through Mc. Under these assumptions, the radiance L at each pixel is computed as:
L L + Lamoke + f + Lphong)
Here, Lise and Lsmooe are accumulated along the ray before reaching the 3DGS, Í is the transmittance describing remaining energy, Las is the 3DGS radiance with charring, and Lphong models fire illumination on the geometry. The contribution of each term is visualized in Fig. Their computation is given in subsequent rendering passes, with details in Appendix A.3
Fire Rendering In fire rendering, the dominant visual effect arises from self-emission, which we model based on Planck's 's blackbody radiation law (Nguyen al. 2002). The absorption coefficient oa is set to a fixed positive value when the reaction coordinate Y >> 0, indicating active combustion, and zero otherwise. Spectral volumetric rendering is then performed by integrating the emission term along the ray, and the resulting spectral distribution is converted to RGB color space with chromatic adaptation, following the approach in (Nguyen et al. 2002), to obtain perceptually plausible colors.
Smoke Rendering Smoke becomes visible as the flame cools down during combustion. We render the smoke when the reaction coordinate variable satisfies Y < Ysmoke- The smoke color is determined by the type of burning material from material reasoning in Section 3. For example, smoke from wood combustion is white, while smoke of burning plastic is black (Fig- 4p). By incorporating this model into the volume rendering pipeline, smoke can be presented along with the fire.

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(a) Original view
(b) Add charring effect
(c) Add smoke
(d) Add fire
(e) Phong illumination
(I) Generative refinement
Figure 5: Rendering Components Breakdown. Starting with the original view (a), we first add the charring effect (b). Next. we incorporate the simulated smoke (c), followed by the simulated fire ( Finally, Phong illumination enhances the ground lighting effect caused by the fire, allowing the originally dark shadow be brightened An optional generative refinement can further enhance the ground reflection
3DGS Rendering To implement the charring effect in 3DGS, we apply a scaling factor to the color of 3DGS points where the relative char mass satisfies M, 2 M Specifically, the color is dimmed by dark Mc-M.t 1-M where dark is user-defined factor (typically less than 1) that controls the degree of color dimming when the char mass reaches its maximum (M₃ 1). This approach allows the charred regions to progressively darken as the char mass increases, visually simulating the accumulation of charring on the material surface.
Phong Illumination We adopt the traditional Phong illumination model (Phong 1998) simulate
the lighting effect of fire on 3DGS. Specifically, we treat voxels with temperatures exceeding given threshold as volumetric light sources. For each 3D Gaussian, we consider only the diffuse and specular components. The accumulated spectral radiance at each wavelength À is:
L2 = Σ, ( Ika (n 1,) + kg (r, v]
where (1) is the spectral radiance emitted by voxel i, n is the surface normal obtained from the normal map rendered by the 3DGS in Section 3.1 1, is the light direction, v is the view direction, and r, the reflection direction. Ka and k, are the diffuse and specular reflection coefficients, and controls the sharpness of the specular highlight. Finally, the accumulated spectral radiance is converted into RGB color space using the same way fire rendering, resulting the perceived illumination effect the 3DGS.
Optional Generative Refinement While method captures key physical aspects of fire, real- world combustion involves additional complexities such as indirect illumination, flickering, and subtle light-material interactions, which remain difficult for thysics-based pipelines. To enhance realism, we introduce an optional generative refinement module based on Wan2. (Wang et al.) 2025). diffusion video model supporting image and text conditioning. Inspired by SDEdit (Meng et al. 2022) and PhysGen (Liu et al., (202c)), we encode the simulated video into the model's latent space, perturb it with noise, and then denoise with the first frame image condition, guided by classifier-fre free guidance (Dhariwal & Nichol 2021; Ho & Salimans 2022). This process adds high-frequency details and more realistic illumination, as shown in Fig. 5f However, it may also alter background content and lacks strong 3D consistency, so we treat it as an optional refinement step and provide further discussion Appendix B.6
4 EXPERIMENTS
In this section, evaluate FieryGS across diverse scenes and compare it with baselines. We further demonstrate the flexible user control of FieryGS. Results highlight FieryGS's strengths in high-fidelin rendering, physical plausibility, and controllable fire synthesis. Please refer to our supplementary video for high-quality dynamic visualizations.
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Table 2: Quantitative comparisons.
Table 3: User Studies results.
Method
Aesthctic Quality
Imaging Quality?
DINO Structure
Baseline
Perceptual Realism
Physical Plausibility
Image
Video
Image
Video
AuX
0.488
0,603
1.04
AutoVFX
77.8
86.6
85.5
Rumway- VVV
0,605
0.701
0.68
Runway-V2V
79.4
66.5
85,3
79.0
InstructGSSGSS
0.451
0.394
0.66
vs Instruct-GS2GSS
85.5
63.0
83.2
84.5
Ours
0.702
0.38
Note: Values = of cases where FieryGS is preferred.
Experimental Details We evaluate FieryGS on 6 real-world scenes, including 4 custom-cant custom-capture scenes (Firewood, Kitchen, Chair, Stool) recorded with an iPhone, the Garden scene from the MipNeRF360 dataset, and the Playground scene from the Tanks and Temples dataset. These scenes cover both indoor and outdoor environments and feature diverse object geometries, materials, and spatial arrangements, validating our method in complex, in-the-wild settings.
We compare FieryGS against 3 representative baselines: an automatic VFX pipeline (AutoVFX (Hsu et al. 2)24)), a video-to-video generation model (Runway- V2V (Runway (2224 and text- driven 3DGS editing method (Instruct-GS2GG (Vachha & Haque 2024)). AutoVFX enables dynamic editing in 3DGS scenes via language instructions using Blender's physics engine. Runway- V2V refers to the leading commercial model of Runway for video-to-video synthesis. Instruct-GS2GS performs text-driven editing on 3DGS models via 2D diffusion model. All support fire synthesis, enabling a comprehensive comparison with our method. All prompts are in Appendix B.1
Qualitative Evaluation Fig. 6 presents a comparison of FieryGS against baselines on Kitchen scene, demonstrating dynamic fire synthesis over time. Runway-V2V produces visually appealing fire videos, but significantly alters the original scene's ' appearance and structure-for for instance, a plate originally placed on the table is transformed into circular groove the tabletop, and Lego bricks tumned into a pile of wooden blocks. Furthermore, its fire lacks physical plausibility, failing to capture core combustion dynamics such flame propagation, and it cannot generate smoke colors that vary with different burning materials. Aut incorporates dynamic fire through Blender's physics engine. However, in complex indoor environments, the resulting flames fail achieve a convincing level of realism. Instruct-GS2GS cannot localize fire edits and supports only static modifications of 3DGS models. In contrast, FieryGS generates temporally coherent fire effects that are both visually authentic and physically grounded, faithfully reproducing the evolution of ignition, flame spread, and scene illumination. More qualitative comparisons are presented in Appendix B.I
Quantitative Evaluation We report Aesthetic Quality and Imaging Quality scores from VBench (Huang et al., (024b) to assess visual fidelity, and DINO Structure Score (Parmar et al., 2024) to evaluate structure preservation. As shown Table our method achieves the highest scores in both visual quality metries and the lowest DINO Structure Score among all baselines, indicating that it produces visually compelling results while faithfully preserving the input scene structure.
User Studies We conducted two user studies to evaluate both the perceptual realism and physical plausibility. In the first study (86 participants), users compared 31 randomly sampled image or video pairs and selected the one with more realistic fire that better preserved the background scene. The second study (88 participants) followed the same setup but asked users to judge which result appeared more physically plausible. Results in Table 3 demonstrate consistent preference for method. Additional setup details are provided in Appendix [35
The average runtime of FieryGS during the simulation and rendering stage 2.37 seconds per frame on an NVIDIA RTX 4090D GPU. A detailed timing breakdown and comparisons with basclines are provided in Appendix
Runtime
User Control Analysis A key advantage of FieryGS is its fine-grained user controllability over combustion behavior. Users can adjust the full combustion-related physical parameters-ignition location, airflow, fire intensity, thermal diffusivity, charring rate, and more. Fig. illustraaes how varying these parameters produces semantically meaningful and physically consistent changes in fire behavior. For example, altering the ignition location results in different flame propagation paths, while adjusting airflow direction directs the spread of flames accordingly. These controls enable precise authoring of dynamic fire effects without manual 3D modeling or complex simulation setup. support only limited,
Compared to baselines, which either lack explicit control (Runway-V2V Searse-grained edits (Fx, Instruct-GS2GS), FieryGS offers a significantly more flexible and intuitive editing workflow for physically plausible fire synthesis.
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Auto VFX
Runway- V2V
Ours Instruct-GS2GG
Figure 6: Fire synthesis results over time on Kitchen scene. AutoVFX shows limited fire realism in complex indoor environments. Runway- V2V generates visually plausible flames but significantly alters the scene and omits ignition dynamies. Instruct-SS2GS produces static, fow-fidelity edits without temporal evolution. In contrast, FieryGS synthesizes physically grounded, eioevevol fire with realistic ignition, spread, and scene illumination.
Original
Intensified
Airflow
Bottom
Behind
Front
Figure 7: Controllability of FieryGS. Rows vary ignition location: under (Bottom), behind (Behind), and in
front of the table (Front). Columns show simulation settings: baseline (Original), increased intensity via stronger buoyancy (f a) and lower reaction rate (1 A) (Intensified), and added rightward wind (Airflow). FieryGS enables intuitive control over ignition, intensity, and airflow.

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5 LIMITATIONS AND CONCLUSIONS
While FieryGS demonstrates strong performance in multi-object scenes, it incorporates several simplifications for efficiency. Specifically, the framework does not explicitly model mass loss or thermal degradation, simplifies certain fire dynamics, and focuses more on multi-object scenes rather than modeling large-scale conflagrations. In addition, the uneven distribution of reconstructed 3DGS points can introduce artifacts, and misclassification in material reasoning may lead incorrect combustion behavior. Despite these limitations, FieryGS provides an automated pipeline for in-the- wild fire synthesis, with broad potential for simulation, safety training, and immersive content. Code and data will be released upon acceptance. For a more detailed discussion of limitations and potential directions for future work, we refer readers to Appendix C
ACKNOWLEDGEMENT
This work is supported by the projects of Beijing Science and Technology Pro- gram(Z251 105008125028).
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. We conducted two user studies on Amazon Mechanical Turk to evaluate perceptual realism and physical plausibility. The studies followed platform guidelines, and no personally identifiable information was collected. Beyond these studies, no human subjects or animal experiments were involved. All datasets used in this work were either publicly available or captured controlled environments, ensuring no violation of privacy or copyright.
One potential societal risk of this research is the misuse of fire synthesis for misinformation or mali- cious visual manipulation. We explicitly acknowledge this risk and strongly encourage responsible and ethical use. At the same time, we believe that high-quality fire synthesis has significant positive applications. It can benefit a wide range of domains, from AR/VR, gaming, and film production to virtual fire drills, heritage preservation, and roboties perception under adverse conditions, by providing controllable, safe, and realistic fire effects without requiring real-world flame generation, thereby reducing potential risks. We are committed to transparency, integrity, and the responsible dissemination of research outcomes.
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APPENDIX OVERVIEW
This appendix provides supplementary materials support and extend the main content of FieryGS. Section A elaborates on implementation specifics for each core component of FieryGS, including scene modeling with combustion property reasoning, combustion simulation, and rendering. Section presents extended experimental results and analyses, covering additional qualitative comparisons, cost and accuracy analyses of combustion property reasoning, runtime and resource usage. user study setup, and the discussion of optional generative refinement, Section Cennu the current limitations and future directions of our method. We further include a supplementary video, showcasing the dynamic fire synthesis results generated by FieryGS.
A METHOD DETAILS
A.1 SCENE MODELING WITH COMBUSTION PROPERTY REASONING
As outlined in Section 3.1 of the main paper, we first reconstruct a high-quality 3DGS model from multi-view images, accurately capturing both the appearance and geometry of the scene. We then segment the 3D Gaussians and infer combustion-relleaat physical properties for each segmented region using multimodal large language model (MLLM). Below, we provide further implementation details segmentation and prompt design.
A.1.1 HDBSCAN HYPERPARAMETER SETUP
To obtain instance-level 3D segments, we employ HDBSCAN (Mclnnes et al. (017) to cluster the fea- ture vectors of 3D Gaussians. We adopt the HDBSCAN parameter settings used SAGA (Cen et 2025). including minimum cluster size of 10 and an epsilon of 0.01. Inspired by GARField (Kim al. 2024), we further construct hierarchy of 3D clusters by recursively applying HDBSCAN at multiple affinity feature malles-specificaall 0.9, 0.5, and 0.1. These parameters were selected through empirical validation and remain fixed across all experiments. We found this configuration to generalize well across the diverse scenes in our dataset.
A.1.2 PROMPTS FOR COMBUSTION PROPERTY REASONING
A carefully crafted combination of visual and textual prompts is critical to enable accurate material reasoning by the MILM.
Inspired by previous work (Xu et al. 2024 we design specialized prompt for GPT-4o tailored to combustion property inference (see Fig. S). The visual prompt includes a set of contextual images, ranging from global to local perspectives: (1) full-scene rendering, (2) the same rendering with the target region highlighted using a bounding box and mask overlay, and (3) an isolated and zoomed-in in view of the segmented region. This visual hierarchy encourages the MLLM to reason about each part in relation to its global spatial context.
The textual prompt guides the model through step-by-step reasoning process: it first generates brief caption describing the segmented region, then selects the most appropriate material type from a predefined material library, and finally infers physical combustion attributes such burnability and thermal diffusivity. This prompt design enables the MLLM to connect local and global visual cues, and incrementally construct semantic understanding of the scene, facilitating accurate physical property inference.
A.2 COMBUSTION SIMULATION
We implement our simulation framework from scratch using the Taichi programming language (Hu et al. 2019), where all variables-include the velocity field reaction coordinate Y. material temperature T... and relative char mass M.-are stored at the center the grid with a resolution of 256 x 256 256, following the convention in (Fernando et al.) (2004). Based on the operator splitting method (Stam 2023) for time discretization, the combustion simulation within a single time step At be summarized follows:
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Original Image
Mask Overlay
Part Image
Provided picture composed of three images arranged from left to right:
Original Image: The original photo of the entire scene.
2. Mask Overlay: A segmentation overlay highlighting the part of interest in blue, with red bounding box. (This may be either the object the background.)
3. Part Image: A cropped and centered view showing only the segmented part.
You may use three images understand what the part and identify whether the segmented part an object or the background. The third image (Part Image) provides the clearest visual of the target part, but context from the first and second images may also be useful for identification.
Based on the picture, firstly provide a brief caption of the part. Secondly, describe what the part is made of (provide the major one). Thirdly, combine what the scene and the material of the part determine whether the part burnable.
Finally, you must provide: the thermal diffusivity ratio (i.e., the material's thermal diffusivity divided by that wood):
Format Requirement:
You must provide your answer as 4-part tuple:
(caption of the part, material of the part, burnable/unburnnalle. thermal diffusivity ratio vs, wood)
include any other text in your answer, will be parsed by code script later.
common material library: (wood, sand, metal, plastic, glass, fabric, foam, food, ceramic, paper, leather, plant, stone, cement, concrete, soil, clay, composite).
yellow toy dump truck bucket, plastic, burnable, 0.2
Figure Visual and textual prompts used in the MLLM-based combustion property reasoning. The visual input includes both global scene context and localized rendering of the segmented region. The accompanying text prompt guides the MLLM through step-by-step reasoning process: first generates brief caption describing the segmented region, then selects most likely material from predefined material library, and finally infers combustion-relee physical properties such as burnability and thermal diffusivity.
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1. Advection. The velocity field and the reaction coordinate variable Y are advected using the Lagi-Laganngian method (Staniforth & Côté 1991):
u SemiLagrangian(uu uu, At, u"),
(5)
Y SemiLagrangian(>" (Y", At, u²).
(6)
2. External Forces and Reaction. We then account for external forces acting on the velocity field u, and for the reaction consumption on Y:
u == u + FAI,
y = Y kåt.
(8)
3. Pressure Projection. To enforce the incompressibillity condition (V u=+1 0), we solve the Poisson equation V2p = V u using Gauss-Seidel iteration to obtain the pressure field
p. The velocity field is then updated as:
p GaussSeidel(u"),
(9)
u ++
u Vp.
(10)
For boundary conditions, we apply open boundary condition on the simulation bounding box. For obstacles, open boundary conditions are used when velocity points outward, while no-through (Neumann) boundary conditions enforeed when velocity points inward. This encourages fluid to flow out of obstacles freely but prevents from entering them.
4. Charring Effect. The material temperature T,, and relative char mass M, are updated explicitly. Since the thermal diffusion term in the update of Tm corresponds to solving Poisson equation, we subdivide the time step into smaller sub-steps to ensure stability. In this way, formulation allows to capture temperature exchange between objects and enables fire propagation between adjacent combustible solids, as illustrated in Fig,
Fire propagation between contacting combustible objects. The three images (left right) show the gradual spread of fire across different objects. They demonstrate that our model accurately captures thermal diffusion, which enables realistic flame transmission between neighboring flammable materials.
Figure 9:
A.3 COMBUSTION RENDERING
To render fire in a physically accurate manner, we first integrate its self-emission spectrum and convert the result into the RGB color space following the approach in (Nguyen et al. [002 Pegoraro Parker (2006). Specifically, the emitted spectral radiance at given wavelength is modeled using Planck's s blackbody radiation law:
2hc
1
(T)
(1))
A5
eAT
where T' denotes the local temperature, and h, c, and are the Planck constant, the speed of light, and the Boltzmann constant, respectively.
To reduce computational cost, the spectral radiance is first converted the CIE XYZ color space using the standard tristimulus curves defined by the Commission Internationale de I l'éclairage (CIE), prior
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to volume rendering integration (Nguyen et al., 2002 Pegoraro & Parker 2006). The integrated XYZ values are then transformed into the LMS cone response space using the M. CAT02 transformation matrix. Chromatic adaptation is applied in this space based on the maximum temperature present in the fire (Nguyen et al.] 2002). Finally, the result is converted back to the RGB color space, followed by gamma correction for display.
To further enhance the quality of volume rendering, we adopt a coarse-to- sampling strategy (Park al., 2021). We first sample 128 points along each ray uniformly, followed by 1024 points via importance sampling based on the reaction coordinate variable Y. Both sets of samples are used for the joint rendering of fire and smoke. To address potential exposure issues when compositing their RGB outputs, we apply ACES tone mapping curve (Narkowicz] (016) to remap the colors into the 0. range. All these rendering procedures are implemented from scratch in Py PyTorch (Paszke et 2019).
B EXTENDED EXPERIMENTAL DETAILS AND RESULTS
B. MORE QUALITATIVE COMPARISONS ACROSS DIVERSE SCENES
Datasets and Baselines We provide additional qualitative comparisons across 6 real-world scenes:
4 custom-captured scenes (Firewood, Kitchen, Chair, Stool), the Garden scene from MipNeRF360. and the Playground scene from Tanks and Temples. For comparison, we consider 3 baselines: VFx (Hsu al. 2024). language-driven automatic VFX pipeline: Runway-V2V (Runway) 2024a by commercial mideo-to-vider generation model; and Instruct-GS2GG (Vachha & Haque 2024). an instruction-barn based 3DGS editing method.
Prompt Design For Runway- V2V, the prompt is "Add fire to the {Target), showing a full burning process from ignition to full blaze to smoldering ashes. Flames gradually grow, engulf the object, then slowly fade as smoke rises and embers glow."; ; for AutoVFX and Instruct-GS2GS, we use a shared prompt: "The {Targed) in the scene is engulfed in roaring flames. The firelight illuminates the surroundings. The smoke billows into the air." In both cases, {Target} refers to the manually specified object be ignited.
More Qualitative Evaluation Beyond the kitchen scene comparison shown in the main paper (Fig. 6) we present qualitative results for the remaining 5 scenes in Figs. 12 T6 Runway-V2V generates visually appealing fire effects but significantly alters the rest of the scene including geom- etry and appearance of both the background and the burning object- and fails to depict physically plausible combustion dynamics such as ignition, spread, and dissipation. Although Aux is based on Blender's built-in physics engine, it is not specifically designed for fire synthesis and lacks fine-grained control over combustion behavior, resulting in limited visual realism. Instruct-GS2GS performs only coarse, static global edits and is not capable of producing realistic dynamic flames.
In contrast, FieryGS produces photorealistic and physically grounded fire effects that faithfully capture the full progression of combustion, including ignition, flame spread, surface carbonization, and eventual burnout.
B.2 COST ANALYSIS OF COMBUSTION PROPERTY REASONING
As described Section 3.1 of the main paper, we employ GPT-4o (Hurst et al. 2024) to perform zero-shot material property reasoning. In our pipeline, the number of API calls corresponds to the number of segmented regions. As summarized in Table 4 FieryGS requires between 9 and 209 calls per scene, depending on scene complexity.
We adopt the ChatGPT-4o-Latest API, which is officially priced at $5 per million input tokens and $15 per million output tokens. On average, each query uses 1,282 input tokens and generates 18 output tokens, resulting in a cost of approximately $0.0066 per call. For a typical scene (mean = 84 calls), the total cost amounts to approximately $0.55.
Overall, our GPT-4o-based reasoning pipeline is highly cost-efficient and substantially more econom- ical than manual annotation.
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Table 4: GPT-4o API call counts per scene for combustion property reasoning
Scene
Firewood
Stool
Chair
Kitchen
Garden
Playground
Avg.
Times
26
46
46
209
169
84
Table 5: Accuracy of MLLM-basee material reasoning across test scenes.
Scene
Firewood
Stool
Chair
Kitchen
Garden
Playground
Avg.
Accuracy (%)
88.46
88.89
82.61
91.30
89.95
89.94
89.31
B.3
ACCURACY ANALYSIS OF COMBUSTION PROPERTY REASONING
Accurate and robust combustion property reasoning is essential for physically plausible fire simulation. Here, quantitatively evaluate the accuracy of our approach.
Since no public scene-level benchmark currently offers reliable ground-truth labels for combustion- relevant materials, we perform manual evaluation on test scenes. Specifically, we annotated the material type for each segmented region and compared these annotations against predictions from the MLLM-based material reasoning module. prediction is deemed correct if matches the ground truth label. As summarized in Table 5 our method achieves an average accuracy of 89.31 % across six diverse scenes, demonstrating strong material reasoning capability.
Most material reasoning errors occur in (i) distant background regions or very small objects that are difficult discern, (ii) heavily occluded areas where the initial segmentation is unreliable, and (iii) occasional reconstruction artifacts in 3DGS that distort texture under the GPT-4o inference view. These limitations are consistent with those of current 3DGS segmentation and vision-language models limitaainn shared by current 3DGS segmentation methods and vision-language models. Nevertheless, the overall accuracy is sufficient to support downstream combustion simulation with minimal human intervention.
B.4 RUNTIME AND COMPUTATIONAL RESOURCES
In our pipeline, 3DGS reconstruction and combustion property reasoning are performed offline, after which pre-frame combustion simulation and rendering are executed. As summarized in Table 6 report detailed runtime breakdown of key components, including combustion simulation, Gaussian splatting rendering, and fire and smoke rendering, on a single NVIDIA RTX 4090D GPU. On average, the simulation and rendering stage memory usage below 10.0 GB, demonstrating that our method both computationally efficient and hardware-friendl. friendll.
2.37 seconds per frame, with peak GPU
Comparison with Baselines Compared to existing baselines, our method offers a favorable balance of speed and visual quality: AFt Hsu et al. (2024) relies on Blender Blender Online Commu mity for simulation and rendering and requires approximately 4-10 10 minutes per frame, making significantly slower than our method. Instruct-GS2GS Vachha & Haque (2024), which directly edits 3DGS, runs at fast speed comparable to vanilla 3DGS. However, it produces only coarse, static edits, making it unsuitable for synthesizing realistic dynamic flames. Runway- V2V Runway (2024a b)) is closed-soures sourcee model, preventing direct runtime comparisons; according to its official website, generating a 10-second video takes about 30 seconds, but while it produces vivid flame effects, often alters the background content and lacks both physical plausibility and parameter controllability.
B.5 USER STUDY SETUP DETAILS
We conduct two studies Amazon Mechanical Turk to assess the key aspects of our method: perceptual realism and physical plausibility. Both studies use an A/B comparison setup, where participants were shown 31 randomly sampled image or video pairs. Each pair included one result
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Table 6: Runtime breakdown (s/frame) of key components in FieryGS across different scenes.
Scene
Firewood
Stool
Chair
Kitchen
Garden
Playground
Avg.
Simulation 1.27
1.33
1.31
2.56
1.30
1.34
1.52
GS Render
0.010
0.0045
0.0077
0.0043
0.034
0.013
0.012
Fire & Smoke Render
0.75
0.90
0.86
0.69
0.45
1.37
0.84
Input
Trial 1
Trial 2
Instruction MUST READ
An artliciall huliignnc agant la trying to add late fire to real world acanes, showing what might look like somathing soure calgght fru. The goall to make the liames, smoka, und lighting look natural and buliovable, while kesping the rust unchanged.
asi two imagesi Video clia chaice:
Thia Pleasee zcom neaded, and consider following when making your
Check how reslletic looks. the
Do fomme and smoke look real?
nearty
Check whether the background wtill looks Input scene. the objects, tecture, and leyout cutside the
area mosily preseroe??
obviousty Repeatodl selecting the option considered
the imagervideo
realistic
Important:
O Trial
O Trtal 2
20 31
Subrrit
Figure 10:
Visualization of interface for user study.
from FieryGS and from baseline, with randomized left- placement to avoid positional bias. An example of the evaluation interface is shown in Fig. IO
Study 1: Perceptual Realism This study involved 86 participants. In each trial, users were asked select the result that exhibited more visually realistic fire effects while maintaining the integrity of the original scene. Results are summarized in Table 3 under "Perceptual Realism" (Image/Video).
Study 2: Physical Plausibility We recruited 88 participants using the same evaluation protocol.
This time, participants were instructed to choose the version that appeared more physically plausible, based on how consistent the fire behavior was with real-world expectations, while also preserving scene structure. Results are reported in Table Junder "Physical Plausibility" (Image/Video).
Across both studies, FieryGS consistently outperforms all baselines in user preference for both images and videos. These results indicate that our method produces fire effects that are not only visually compelling but also more aligned with human perception of physical realism.
B.6 DISCUSSION OPTIONAL GENERATIVE REFINEMENT
To balance efficiency and realism, we simplify our combustion simulation and rendering pipeline by omitting certain computationally intensive modules. As result, method struggles to capture some high-frequency visual effects, such as complex lighting interactions (e.g., multi-bounce reflections), fine-scale flame textures, and realistic charring patterns.
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Temporal inconsistency in generative refinement fire sequence. While the fire visually improves realism, the underlying table caturr-oocluude during peak fire- fire-changes after the flame dissipates, revealing the diffusion model's limitations in preserving scene consistency over longer time spans.
Figure 11:
To address these limitations, we introduce video refinement module based on pre-trained diffusion- based generative model (Wan2.1 (Wang et al.) 2025 ), described in Section 3.4. Rather than replacing physics-based simulation, this model is used to complement it-enhancing visual fidelity while preserving physically grounded motion. practice, we find that this refinement leads to more natural lighting, sharper flame boundaries, and a more compelling overall appearance.
While the refinement model improves visual quality in many aspects, can introduce two notable side effects that warrant further investigation. First, selectively enhancing fire effects without affecting the background is inherently difficult. Generative models tend to alter surrounding areas along with the target region, and due to the complex and diffuse nature of flame boundaries, masking proves unreliable. Second, as illustrated in Fig. [I] maintaining temporal and 3D consistency remains a challenge, especially for long videos- limitation rooted in the current capabilities of generative video models themselves.
In summary, while generative refinement opens up new possibilities for achieving photorealistic fire videos, it is still a complementary step that must be carefully integrated with physically-baser simulation. We view this as a promising direction for future research, particularly generative video models continue to evolve in quality and controllability.
C DISCUSSION OF LIMITATIONS
Although FieryGS performs effectively on object-level scenes, also exhibits several limitations that affect both the physical realism of simulated fire and the generality of the framework.
Material Degradation and Mass Loss To maintain efficiency, do not simulate mass loss or thermal degradation, such as shrinkage, crumpling, or disintegration. Prior works (Liu et al.) 2024a Larboulette et al. 2013) attempt capture these phenomena but at high computational cost and with complex, manually ly intensive frameworks. Accurately modeling structural changes with unknown internal material properties (e.g.. weakening collapse) remains extremely challenging (Lakkonen) 2024
Xu & Nan 2024), representing a significant avenue for future work.
Simplified Flame and Charring Behavior While our model captures turbulent flames and smoke, simplifies detailed physical processes for efficiency. For example, do model how flames ignite surrounding materials, and more physically grounded approaches such as the thin flame model (Nguyen et al.] 2002) could better capture dynamic fire behavior. These are important directions for improving the physical fidelity of the simulation.
Limitations in Scene Scale FieryGS is currently tailored object-level scenes and cannot be directly applied to large-scale scenarios, such as forest or building fires. Extending the framework would require redesigning the fire modeling pipeline and solving new governing equations (Hädrich ct al. 202I)
Non-Uniform 3DGS Distribution
The reconstructed 3DGS points are unevenly distributed, pri- marily concentrated obstacle surfaces, which can introduce artifacts in volumetric simulation and
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AutoVFX
Runway- V2V
Ours Instruct-GS2GS
Figure 12: Fire synthesis results over time Firewood scene. AutoVFX produces unrealistic fire and smoke. Runway-V2 V2V generates visually realistic fire, but it completely alters the scene and lacks a gradual ignition process, showing only fully developed flames. Instruct-GS2GS produces static and unrealistic results. In contrast, FieryGS generates realistic, time-evolving fire with natural ignition and growth process.
rendering. Achieving more uniform distribution throughout obstacle volumes is therefore another important direction for improvement.
Misclassifications in Material Reasoning As discussed in Section B.3 while FieryGS achieves high accuracy in material property reasoning, misclassifications occur in (i) tiny or distant background objects with limited visual cues, (ii) heavily occluded regions where segmentation quality degrades, and (iii) occasional 3DGS reconstruction artifacts that distort appearance in the GPT-4o inference view; these issues are inherent limitations of current 3DGS segmentation and vasion-language models, and future work will focus on improving robustness and reliability under low visibility and occlusion, well resilience reconstruction imperfections.
Despite these limitations, FieryGS provides an automated approach for fire synthesis in complex scenes, enabling applications in simulation, safety, and immersive content creation. Future work will aim to address these constraints to enhance both physical fidelity and scene generalization.
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Auto
Instruct-GS2e GSSG- Runway- V2V
Ours
Figure 13:
Fire synthesis results over time Stool scene. AutoVFX yields visually implausible results, with exaggerated flames and smoke. Runway- V2V produces looistic-lookigg fire, heavily distorts the scene geometry and skips the ignition phase, showing only fully developed flames. Instruct-GS2e5 outputs blurry, static edits without dynamic bchavior. In contrast, FieryGS produces physically plausible, temporally coherent fire that evolves naturally from ignition to flame spread and decay.
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AutoVFX
-VV V2V
Runway-
Ours Instruct-GS2GS
Figure 14: Fire synthesis results over time on Chair scene. AutoVFX exhibits exaggerated and implausible fire behavior, with little integration into the scene. Run produces visually plausible flames but significantly modifies the scene's appearance and omits the ignition phase. Instruct-GS2GS yields static, glowing effects lacking realistic dynamics. In contrast, FieryGS produces physically grounded fire that evolves naturally-capturie capturing ignition, spread, and burnout while preserving the underlying seene.
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AutoVFX
Runway- V2V
Instruct- GS2uu
Ours
Fire synthesis results time Garden scene. AutoVI-X produces unrealistic, oversized flames and dense smoke that fail to integrate with the environment. Runway-V2 V2V generates visually compelling fire but alters scene details and skips the ignition phase, displaying only intense, fully developed flames. Instruct-SS2GS results in static, overly saturated outputs with no temporal dynamics. In contrast, FieryGS produces physically plausible fire that evolves naturally over time time-capturing ignition, spread, and gradual decay decay-while preserving the original context.
Figure 15:
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AutoVFX
Runway- V2V
Ours Instrutt-GS2GS
Fire synthesis results over time on Playground scene. AutoVFX generates exaggerated fire and dense smoke that appear detached from the physical structure. Runway- V2V produces high-quality flames but drastically alters the geometry and texture of the playground, lacking any notion of progressive igmition. Instruct-GS2GS results in temporally static and visually distorted outputs. In contrast, FieryGS synthesizes physically realistic fire that evolves smoothly over time, preserving scene structure while capturing natural ignition, flame spread, carbonization, and decay.
Figure 16:
26


Reviews:
[{"review_id": "gDGQKRPIiU", "reviewer": "Reviewer_EZSh", "summary": "This work presents a system named “Physics-Integrated Gaussian Splatting,” aiming to synthesize \ncontrollable and physically inspired fire and combustion effects in real-world 3D scenes.  The method integrates several coordinated modules to form a complete pipeline from real-scene  reconstruction to fire simulation:\n 1) a multimodal large model predicts material types and combustion-related parameters in 2D projection \nspace and back-project them to 3D Gaussian\n 2) the 3D scene is voxelized, where solid and air regions are distinguished by Gaussian density—simplified\n fire simulations are applied to air voxels, while thermal diffusion and charring are modeled for solids, with \nintuitive user controls such as ignition position and airflow\n 3) a unified rendering framework generates multiple visual effects, including flames, smoke, charring, and \nindirect illumination.\n \nThe work does not introduce a new rendering theory or physical model but integrates existing techniques\n 3D Gaussian Splatting, simplified fluid simulation, and multimodal reasoning—into a complete interactive\n pipeline, producing visually realistic fire results.\n\n Experiments on multiple real and synthetic scenes demonstrate visually realistic and controllable fire \ngeneration, and the results appear to outperform existing generation-based approaches", "questions": "- How does the system perform on uncommon materials, composite materials, or objects with unusual textures?\n\n- Could the authors clarify or quantify how much these physical simplifications affect the realism of the generated fire?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper presents an integrated system combining 3D Gaussian Splatting, simplified physical simulation, and multimodal reasoning to construct a physically-informed and controllable fire generation pipeline. \n\nThis integration and simplification provide a certain degree of novelty in application, making complex fire simulation more practical and easier to operate. The system is well-designed, with modules for material prediction, fire simulation, and rendering fire results. Experiments on multiple real and synthetic scenes demonstrate that the generated fire is visually plausible and controllable, outperforming existing generation-based methods. The user interaction design further enhances the system’s operability. \n\nThe paper provides clear and understandable descriptions of the system modules  and workflow, and the authors indicate that the source code will be released in the future, which facilitates follow-up \nresearch. Overall, this work offers a complete and practical solution for interactive fire generation, with utility for computer graphics, visual effects, and virtual environment fire simulation", "weaknesses": "While the integration offers practical value, the method has limited theoretical and technical novelty.  Moreover, the approach heavily depends on the MLLM’s 2D material inference capability, and its performance on uncommon materials, composite materials, or extreme fire conditions remains unexplored. \nAdditionally, the paper does not provide comparisons between the simplified physical simulation and a full \nphysics-based simulation, making it difficult to justify the acceptability of the simplifications in practice. Finally\n While FieryGS accounts for multiple effects of fire combustion, it does not explicitly capture or evaluate the \ndynamic lighting effects generated by fire, which contribute to the perceived motion and liveliness of the \nscen", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xqTXWSDWJY", "reviewer": "Reviewer_FTxR", "summary": "The paper present FireyGS, which is a framework that physically simulates fires and charring of objects in real-world images. The process is fully automated with some user control. \n\nThis is achieved through:\n1) making volumetric representation of the real-world scene using 3D Gaussian Splatting, \n2) letting a multi-modal LLM reason about the material characteristics of objects, such as whether they are consumable by fire, \n3) physically simulate fire and smoke using the Navier-Stokes equations while also modelling charring, and \n4) rendering the fire using Phong Illumination and an optional generative model. \n\nThis allows to easily integrate fires into real-world scenes with some user control, such as adjusting fire intensity and airflow, which is quite impressive. The rendering time is very short. The evaluation shows that state-of-the-art methods are far from achieving this.", "questions": "* On line 126, it is implied that the method presented in the paper “maintains physical accuracy”. Is maintaining physical accuracy a good term? The reason I ask is that I would assume that this would be evaluated in some way. It is not, and it is not easy to do either as far as I am concerned. \n* The charring effect is very nice. Why would you not let the fire consume the fuel (material) in each voxel? Have you tried? In theory, this should be straight forward. Are there issues with the visualization?\n* It is stated on line 215: “Focusing on efficiency, our method simplifies processes …”. Could you please be explicit about which processes? I would like to understand exactly what you mean. \n* Figure 5f: While the generative refinement improves illumination shadows, it seems to me that the flames get bleaker with less details. Is this true and a problem in general, or is it just this image? What is going on here - if it is the case? Please help me understand.\n* Line 409: “A detailed timing breakdown and comparisons with baselines are provided …”. It would be nice to see the running times for the baselines as well.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "* The paper is well written and structured well with many images illustrating the capabilities of the method. \n* The results are quite impressive. The paper illustrates how fire can be put into realistic images with little effort. \n\nThe fact that one “easily” can add simulated fires to real-world images is quite astonishing to me. I am very impressed by the results.", "weaknesses": "* The novelty is limited to using an LLM for reasoning about material characteristics and putting well known methods together. If I get this right, there is nothing new about the 3D Gaussian Splatting method, the fire and charring simulation, nor the illumination method. \n* While the results look good, they are far from realistic, especially for larger, more turbulent fires. This can easily be seen by comparing Figure 2 (real-world, live fire) and the images generated by the framework. The smoke and turbulence in the real-world images makes for deeper and visually more “exciting” flames. This is probably because the low resolution of the fire, also the turbulence is not modelled well by the simplified Navier-Stokes. This method works fairly well for small fires with little turbulence – in my opinion. The graphics community, however, have many methods and tricks that would help here, such as dynamic meshes that allows detail to be increased in the areas where there is turbulence. Other more complex and realistic physical simulations have been proposed as well. This would if course hamper computational performance, which is really good for the method presented here. The good thing though is that there are many ways to improve it based on related work in physics-based animation of fire. \n* Some parts could be better explained. It is not clear to me what exactly is fed to GPT4o to reason about material characteristics. I would assume the real-world images, but it is not clear to me from the text.  \n\nOn one hand, while the results are impressive, I wonder if the novelty falls short. This paper represents a fairly small step in an evolution, not a revolution. On the other hand, one could easily see that the significance of this work could be high, as it shows how fire could be simulated in real-world scenes. \n\nWhile the rendering of the fire leaves much to be wanted (think about the vivid fires and explosions in special effects such the opening scene of Star Wars III: Revenge of the Sith – from 2005 where explosions also are simulated using physics), this does not affect my recommendation much, as I expect this to be improved quickly by future work. \n\nBecause of this I lean towards acceptance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "n09FHakw0T", "reviewer": "Reviewer_bvZV", "summary": "The paper propose a method to add fire simulation in 3DGS scenes. It first use MLLM to segment flamable objects and then add fire and charring simulation, with good user controllability, including fire intensity, air flow, and color of the fire based on the material.. The final rendering is a combination of flame simulation with phone shading + 3DGS. Experiments show that the proposed method has better visual quality comparing to exisitng baselines. It also proposes an extension with existing generative models to improve photorealism.", "questions": "1. Is it possible to improve the quality of rendering and fire simulation for the weakness points?\n2. What are the aesthetic qualtiy metric? How it is computed?\n3. Could u show the visualization of the fire in different control parameters?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-written and easy to understand.\n2. The method of using a combination of MLLM and fire/charring simulation is reasonable and easy to implement.\n3. The method provides good controllability of the scene setup, esp in controlling the fire color based on the material.", "weaknesses": "1. The rendering is one-way. It does not consider the shading of fire on surrounding. Thus causing visual artifacts. Though the generative model may solves the problem in some cases, it may alters the background.\n2. The fire simulation does not consider geometry constraint. For example, fire in a building seeing through windows. The simulation of fire / smoke should consider geometry constraints induced by voxels that are \"solid\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sCQDP2110K", "reviewer": "Reviewer_KF7H", "summary": "FieryGS synthesizes realistic, physically grounded fire effects in real 3D scenes by integrating combustion simulation and rendering into the 3D Gaussian Splatting pipeline. It combines scene reconstruction, material reasoning via multimodal LLMs, efficient volumetric combustion simulation,, and unified rendering. The effectiveness of FieryGS is demonstrated by detailed experiments, comprehensive user studies, and visualizations.", "questions": "### Main Question:\n- [Comparison to expert-level tools] Since the physical properties can be inferred by MLLMs and the geometry can be modeled by 3DGS, I wonder if all the input required by VFX/CFD (e.g. meshes, material) can also be provided by MLLMs (Line 237). If so, how is the quality and cost comparison between the proposed combustion simulations and expert-level tools? This comparison is more apples-to-apples and can help the readers understand how much the simplified combustion simulation affects the performance.\n\n### Minor Question\n- [MLLM comparisons] Is GPT-4o necessary for high-performance? Comparisons with other frontier MLLMs in Table 2/5 and Figure 6 might help readers understand more about how useful the language instructions are.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method offers a novel, user-friendly, and highly efficient pipeline for realistic fire rendering in complex scenes. By integrating MLLM–based material reasoning with 3DGS–based segmentation, the approach effectively reduces the need for manual parameter tuning and eliminates dependence on traditional expert-level simulation tools. This design democratizes physically-plausible combustion synthesis, allowing users to achieve compelling visual results with minimal technical overhead. The paper is clearly written and well-structured. Extensive quantitative experiments and qualitative visual analyses robustly support the claims.", "weaknesses": "I do not have substantial complaints about this paper. I move my concerns to \"Questions\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)