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arxiv: 2605.00177 · v1 · submitted 2026-04-30 · 💻 cs.GR · cs.CV

Recognition: unknown

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

Qianfan Shen , Ningxiao Tao , Qiyu Dai , Tianle Chen , Minghan Qin , Yongjie Zhang , Mengyu Chu , Wenzheng Chen
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Baoquan Chen
Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:48 UTC · model grok-4.3

classification 💻 cs.GR cs.CV
keywords fire synthesisGaussian splattingcombustion simulationphysics-based rendering3D scene reconstructionmaterial reasoningvolumetric effectscontrollable simulation
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The pith

FieryGS integrates combustion physics into 3D Gaussian Splatting to generate realistic, controllable fire directly in reconstructed real-world scenes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a single pipeline that reconstructs scenes, infers material properties, runs combustion simulation, and renders the results together. This removes the separate handcrafted geometry and expert parameter tuning required by conventional fire simulation methods. A sympathetic reader would care because the result scales fire effects to any captured indoor or outdoor environment while keeping the fire behavior consistent with the scene's geometry and materials. The approach also gives users direct control over ignition points, intensity, and airflow without breaking the physical consistency.

Core claim

By coupling a multimodal large-language-model module that reasons about material combustibility with an efficient volumetric combustion simulator and a renderer that operates jointly on both fire and scene Gaussians, the framework automatically produces flame propagation, smoke dispersion, and surface carbonization that remain consistent with the reconstructed geometry and inferred material properties.

What carries the argument

The three-module pipeline that links language-model material reasoning, volumetric combustion simulation, and unified rendering inside the 3D Gaussian Splatting representation.

If this is right

  • Fire effects can be added to any 3D Gaussian Splatting reconstruction without manual geometry creation or parameter tuning.
  • Complex combustion phenomena such as flame spread, smoke movement, and surface charring arise automatically from the simulation.
  • Users retain precise control over parameters including fire intensity, ignition location, and airflow while the results stay physically grounded.
  • The method produces higher visual realism and physical fidelity than prior separate reconstruction and simulation pipelines on diverse indoor and outdoor scenes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same coupling of inference, simulation, and rendering could be applied to other dynamic effects such as fluid flow or material degradation within the same scene representations.
  • If the material-reasoning step generalizes, the framework could support predictive uses such as safety planning or virtual training where fire outcomes must be consistent with real geometry.
  • A direct comparison against scenes with known ground-truth material properties would test how sensitive the overall fire output is to small errors in the language-model inference.

Load-bearing premise

The language model correctly infers combustion-relevant material properties from scene images so that simulation errors do not accumulate.

What would settle it

A controlled test in which independently measured material properties of a real scene are compared against the model's inferences, followed by side-by-side visual and quantitative comparison of the simulated fire behavior against recorded footage from the same scene.

Figures

Figures reproduced from arXiv: 2605.00177 by Baoquan Chen, Mengyu Chu, Minghan Qin, Ningxiao Tao, Qianfan Shen, Qiyu Dai, Tianle Chen, Wenzheng Chen, Yongjie Zhang.

Figure 1
Figure 1. Figure 1: FieryGS synthesizes physically-grounded fire effects from multi-view image, enabling controllable and realistic fire for in-the-wild scenes. *Joint first authors † Project lead ‡Corresponding authors 1 arXiv:2605.00177v1 [cs.GR] 30 Apr 2026 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Left: Real-world combustion in a live-fire drill (5280 Fire Science); Right:Full-scale combustion test measuring flame spread time (Zhang et al., 2021) [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall Pipeline of FieryGS. Given multi-view images as input, we first apply PGSR (Chen et al., 2024) to reconstruct scenes with high-quality normal and depth. Next, we leverage MLLM to 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 … view at source ↗
Figure 4
Figure 4. Figure 4: Combustion Property Reasoning. Given an RGB input (a), our method reliably predicts material types (b) and burnability (d). In a complex region with metal spoons inside a mug surrounded by various materials, the 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—… view at source ↗
Figure 5
Figure 5. 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 (d). Finally, Phong illumination enhances the ground lighting effect caused by the fire, allowing the originally dark shadow to be brightened (e). An optional generative refinement can further enhance the ground reflection (f… view at source ↗
Figure 6
Figure 6. 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 dynamics. Instruct-GS2GS produces static, low-fidelity edits without temporal evolution. In contrast, FieryGS synthesizes physically grounded, time-evolving fire with realistic ignition, spr… view at source ↗
Figure 7
Figure 7. 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 (↑ α) and lower reaction rate (↓ k) (Intensified), and added rightward wind (Airflow). FieryGS enables intuitive control over ignition, intensity, and airflow. 9 [PITH_FULL_IMAGE:figures… view at source ↗
Figure 8
Figure 8. Figure 8: Visual and textual prompts used in the MLLM-based combustion property reasoning. The visual input includes both global scene context and a localized rendering of the segmented region. The accompanying text prompt guides the MLLM through a step-by-step reasoning process: it first generates a brief caption describing the segmented region, then selects the most likely material from a predefined material libra… view at source ↗
Figure 9
Figure 9. Figure 9: Fire propagation between contacting combustible objects. The three images (left to 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. A.3 COMBUSTION RENDERING To render fire in a physically accurate manner, we first integrate its self-emiss… view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of interface for user study. from FieryGS and one from a baseline, with randomized left–right placement to avoid positional bias. An example of the evaluation interface is shown in [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Temporal inconsistency in generative refinement across a fire sequence. While the fire visually improves realism, the underlying table texture—occluded during peak fire—changes after the flame dissipates, revealing the diffusion model’s limitations in preserving scene consistency over longer time spans. To address these limitations, we introduce a video refinement module based on a pre-trained diffusion￾b… view at source ↗
Figure 12
Figure 12. Figure 12: Fire synthesis results over time on Firewood scene. AutoVFX produces unrealistic fire and smoke. Runway-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 a natural ignition and growth pro… view at source ↗
Figure 13
Figure 13. Figure 13: Fire synthesis results over time on Stool scene. AutoVFX yields visually implausible results, with exaggerated flames and smoke. Runway-V2V produces realistic-looking fire, but heavily distorts the scene geometry and skips the ignition phase, showing only fully developed flames. Instruct-GS2GS outputs blurry, static edits without dynamic behavior. In contrast, FieryGS produces physically plausible, tempor… view at source ↗
Figure 14
Figure 14. Figure 14: Fire synthesis results over time on Chair scene. AutoVFX exhibits exaggerated and implausible fire behavior, with little integration into the scene. Runway-V2V 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… view at source ↗
Figure 15
Figure 15. Figure 15: Fire synthesis results over time on Garden scene. AutoVFX produces unrealistic, oversized flames and dense smoke that fail to integrate with the environment. Runway-V2V generates visually compelling fire but alters scene details and skips the ignition phase, displaying only intense, fully developed flames. Instruct-GS2GS results in static, overly saturated outputs with no temporal dynamics. In contrast, F… view at source ↗
Figure 16
Figure 16. Figure 16: 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 ignition. Instruct-GS2GS results in temporally static and visually distorted outputs. In contrast, FieryGS synth… view at source ↗
read the original 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. Project page can be found at https://pku-vcl-geometry.github.io/FieryGS/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes FieryGS, a framework for synthesizing photorealistic and physically plausible fire effects in reconstructed 3D scenes captured in the wild. It integrates three modules: a multimodal LLM for reasoning about physical material properties from scene data, an efficient volumetric combustion simulation for generating fire dynamics, and a unified renderer that combines the simulated fire with 3D Gaussian Splatting representations of the scene. The approach aims to eliminate manual parameter tuning and expert intervention, enabling controllable fire synthesis consistent with scene geometry and materials, including phenomena like flame propagation and smoke dispersion.

Significance. If the results hold, this work would be significant for the field of computer graphics and visual computing. It addresses a key limitation in current scene reconstruction techniques by incorporating physics-based simulation directly into the pipeline, potentially enabling more realistic and scalable visual effects for applications in film, gaming, and virtual environments. The unification of reconstruction, reasoning, simulation, and rendering represents a step towards more automated and physically grounded content creation.

major comments (3)
  1. [§3.1 (Material Reasoning Module)] §3.1 (Material Reasoning Module): The description of the multimodal large-language-model-based physical material reasoning lacks specific details on the LLM model employed, the prompting strategy used to infer combustion-relevant properties (e.g., ignition thresholds, burn rates), and any form of validation or error analysis against ground-truth material data. Since this module directly informs the simulation parameters, inaccuracies here would propagate and undermine the claims of physical fidelity and automatic generation without manual tuning.
  2. [§4 (Experiments and Evaluation)] §4 (Experiments and Evaluation): The evaluation asserts that FieryGS outperforms comparative baselines in visual realism, physical fidelity, and controllability across indoor and outdoor scenes, but no quantitative metrics, ablation studies, error analyses, or detailed descriptions of the baselines are provided. This absence makes it impossible to assess the strength of the central claims regarding outperformance.
  3. [§3.2 (Volumetric Combustion Simulation)] §3.2 (Volumetric Combustion Simulation): While the simulation is described as efficient and capable of complex phenomena, there is no discussion of how the parameters inferred by the LLM are mapped to the simulation inputs or any sensitivity analysis showing robustness to potential inference errors.
minor comments (2)
  1. [Abstract] Abstract: The abstract mentions 'precise user control over fire intensity, airflow, ignition location and other combustion parameters' but does not specify the interface or mechanism for this control in the main text.
  2. [References] References: Ensure all cited works on 3DGS and combustion simulation are up-to-date and include recent relevant papers on physics-informed neural rendering.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below. Where the comments identify gaps in detail or evaluation, we agree that revisions are warranted and will strengthen the manuscript's clarity, reproducibility, and evidential support.

read point-by-point responses

  1. Referee: §3.1 (Material Reasoning Module): The description of the multimodal large-language-model-based physical material reasoning lacks specific details on the LLM model employed, the prompting strategy used to infer combustion-relevant properties (e.g., ignition thresholds, burn rates), and any form of validation or error analysis against ground-truth material data. Since this module directly informs the simulation parameters, inaccuracies here would propagate and undermine the claims of physical fidelity and automatic generation without manual tuning.

    Authors: We agree that additional specificity is required. In the revised manuscript we will name the exact multimodal LLM, reproduce the full prompting templates (including how combustion properties such as ignition thresholds and burn rates are elicited), and add a validation subsection that reports agreement with available ground-truth material data or quantifies inference error. These changes will directly address concerns about error propagation and the claim of fully automatic, tuning-free generation. revision: yes

  2. Referee: §4 (Experiments and Evaluation): The evaluation asserts that FieryGS outperforms comparative baselines in visual realism, physical fidelity, and controllability across indoor and outdoor scenes, but no quantitative metrics, ablation studies, error analyses, or detailed descriptions of the baselines are provided. This absence makes it impossible to assess the strength of the central claims regarding outperformance.

    Authors: We acknowledge that the current evaluation section is primarily qualitative. We will revise §4 to include quantitative metrics (e.g., perceptual and physics-based error measures), detailed baseline descriptions, ablation studies that isolate each module, and error analyses. These additions will provide the necessary evidence to support the outperformance claims. revision: yes

  3. Referee: §3.2 (Volumetric Combustion Simulation): While the simulation is described as efficient and capable of complex phenomena, there is no discussion of how the parameters inferred by the LLM are mapped to the simulation inputs or any sensitivity analysis showing robustness to potential inference errors.

    Authors: We will expand §3.2 with an explicit parameter-mapping subsection that describes how each LLM-inferred property (ignition threshold, burn rate, etc.) is converted into simulation inputs. We will also add a sensitivity analysis that perturbs the inferred values within plausible error ranges and reports the resulting variation in flame behavior and visual output, thereby demonstrating robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; independent modules integrated without self-referential reduction

full rationale

The paper describes a three-module pipeline (LLM-based material reasoning, volumetric combustion simulation, unified renderer) that takes scene reconstruction as input and produces fire dynamics as output. No equations, derivations, or fitted parameters are presented that reduce any claimed prediction or result to the inputs by construction. The LLM module supplies external inferences to the simulation; the simulation and renderer are treated as standard physics/graphics components. No self-citations are invoked as load-bearing uniqueness theorems, no ansatzes are smuggled, and no known results are renamed as novel derivations. The framework is self-contained against external benchmarks for its integration claim.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities are stated. The framework implicitly assumes standard validity of 3DGS reconstruction, CFD-style combustion models, and LLM material inference.

axioms (2)
  • domain assumption 3D Gaussian Splatting accurately reconstructs scene geometry and appearance from images.
    Invoked as the base for integrating fire simulation without detailing reconstruction errors.
  • domain assumption Volumetric combustion simulation produces physically accurate dynamics when coupled to the reconstructed scene.
    Central to the claim of physical plausibility.

pith-pipeline@v0.9.0 · 5597 in / 1270 out tokens · 33818 ms · 2026-05-09T19:48:36.137845+00:00 · methodology

discussion (0)

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