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arxiv: 2507.12156 · v3 · submitted 2025-07-16 · 💻 cs.GR

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models

Chen Li , Shanshan Dong , Sheng Qiu , Jianmin Han , Yibo Zhao , Zan Gao , Taku Komura , Kemeng Huang This is my paper

Pith reviewed 2026-05-19 04:40 UTC · model grok-4.3

classification 💻 cs.GR
keywords smoke reconstructionsingle viewdiffusion modelsnovel view synthesisfluid simulationdensity fieldvelocity estimationNavier-Stokes
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The pith

SmokeSVD reconstructs dynamic 3D smoke from a single video using diffusion models for side views and progressive physical refinement.

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

The paper proposes SmokeSVD as a framework that reconstructs dynamic smoke volumes from one video input. It first uses a diffusion-based synthesizer guided by velocity constraints to create consistent side-view images frame by frame. These views then feed into a multi-stage process that iteratively renders and improves images from wider angles while building the 3D density field. The final step applies differentiable advection with the Navier-Stokes equations to recover detailed density and velocity. A reader would care because prior single-view fluid methods required slow optimization under severe ambiguity, and this pipeline aims to deliver usable results more efficiently.

Core claim

SmokeSVD is an efficient and effective framework to progressively reconstruct dynamic smoke from a single video by integrating the generative capabilities of diffusion models with physically guided consistency optimization. It first proposes a physically guided side-view synthesizer based on diffusion models that explicitly incorporates velocity field constraints to generate spatio-temporally consistent side-view images frame by frame. It then iteratively refines novel-view images and reconstructs 3D density fields through a progressive multi-stage process that renders and enhances images from increasing viewing angles. Finally it estimates fine-grained density and velocity fields via diffe

What carries the argument

Physically guided side-view synthesizer based on diffusion models that incorporates velocity field constraints to generate spatio-temporally consistent side-view images frame by frame.

If this is right

  • Produces high-quality multi-view image sequences from single-view input.
  • Yields fine-grained 3D density and velocity fields usable for re-simulation.
  • Supports downstream applications such as editing or physical re-play.
  • Runs with better computational efficiency than prior optimization-heavy approaches.

Where Pith is reading between the lines

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

  • The progressive refinement loop could be adapted to other sparse-view fluid problems such as fire or liquid reconstruction.
  • Real-time video capture pipelines might incorporate the side-view synthesizer to enable live 3D smoke monitoring.
  • The velocity-constrained diffusion step might transfer to non-fluid domains where temporal consistency is the main bottleneck.

Load-bearing premise

The physically guided side-view synthesizer based on diffusion models can generate spatio-temporally consistent side-view images frame by frame that sufficiently alleviate the ill-posedness of single-view reconstruction.

What would settle it

A direct test would compare the synthesized side-view smoke motion against ground-truth multi-view captures; visible temporal flickering or motion mismatch across frames, or failure of the final 3D density field to satisfy Navier-Stokes advection in controlled validation, would show the consistency step does not resolve the ill-posedness.

Figures

Figures reproduced from arXiv: 2507.12156 by Chen Li, Jianmin Han, Kemeng Huang, Shanshan Dong, Sheng Qiu, Taku Komura, Yibo Zhao, Zan Gao.

Figure 1
Figure 1. Figure 1: By leveraging physics-aware diffusion and refinement modules, our method progressively performs novel view synthesis (b) and 3D reconstruction [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our proposed system. For clarity, we categorize the view angles into three types: the input as the front view ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The procedure for side-view synthesis and novel view refinement. First, we use SvDiff to predict side-view images based on the input and previously [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Frame-by-frame training of the side-view synthesizer via feature fusion of adjacent frames. In the forward diffusion process, a clean image [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results at the 80th time step from the ablation study on [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study on novel view refinement. From top to bottom is the [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison at the 80th time step based on different methods on the ScalarFlow dataset. Our method matches the appearance pattern of the input image at the front view, and produces a reasonable shape in the side view. View ∠45 ◦ GlobTrans NGT Ours PICT PINF View ∠135 ◦ [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 7
Figure 7. Figure 7: The refined results on more novel views. Each row, from left to right, [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison at 80th time step on the synthetic dataset [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 8
Figure 8. Figure 8: The progressive scheme for novel view refinement begins with clear [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 13
Figure 13. Figure 13: Ablation study of the refinement model. Each row, from left to right, [PITH_FULL_IMAGE:figures/full_fig_p011_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: We combine the NGT method with our refinement model, the figure [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: We combine the NGT method with our reconstruction model, the [PITH_FULL_IMAGE:figures/full_fig_p011_15.png] view at source ↗
Figure 1
Figure 1. Figure 1: Side-view generation results affected by cumulative error. [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Rendering results of coarse-grained density field, which exhibits [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The architecture of density generator. The illustration depicts the [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The rendering results of reconstructed density field at multiple views based on our proposed method. [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Reconstruction result for a multi-plume scenario, shown from both input and side views. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Reconstruction results for a bunny-shaped smoke scenario without inflow, shown from both input and side views. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The rendered re-simulation results and velocity estimation visualization at the input view and the side view. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The re-simulation result with added fluid-solid coupling (top row), where we place a sphere obstacle (the red circle) at the [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: 3DGS results (top) based on our synthesized novel views (bottom). [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: 3DGS results (top) and our reconstruction result (bottom) under rotating views from [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison of side view synthesis with different frame [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Qualitative comparison of density generators with different numbers of views on the synthetic dataset. [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of the maximum values of reconstructed velocity fields [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
read the original abstract

Reconstructing dynamic fluids from sparse views is a long-standing and challenging problem, due to the severe lack of 3D information from insufficient view coverage. While several pioneering approaches have attempted to address this issue using differentiable rendering or novel view synthesis, they are often limited by time-consuming optimization under ill-posed conditions. We propose SmokeSVD, an efficient and effective framework to progressively reconstruct dynamic smoke from a single video by integrating the generative capabilities of diffusion models with physically guided consistency optimization. Specifically, we first propose a physically guided side-view synthesizer based on diffusion models, which explicitly incorporates velocity field constraints to generate spatio-temporally consistent side-view images frame by frame, significantly alleviating the ill-posedness of single-view reconstruction. Subsequently, we iteratively refine novel-view images and reconstruct 3D density fields through a progressive multi-stage process that renders and enhances images from increasing viewing angles, generating high-quality multi-view sequences. Finally, we estimate fine-grained density and velocity fields via differentiable advection by leveraging the Navier-Stokes equations. Our approach supports re-simulation and downstream applications while achieving superior reconstruction quality and computational efficiency compared to state-of-the-art methods.

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

1 major / 2 minor

Summary. The manuscript presents SmokeSVD, a framework for reconstructing dynamic smoke from a single-view video. It first employs a physically guided side-view synthesizer based on diffusion models that incorporates velocity-field constraints to generate spatio-temporally consistent novel views frame by frame. This is followed by an iterative progressive multi-stage process that renders and refines images from increasing viewing angles to produce high-quality multi-view sequences and reconstruct 3D density fields. Finally, differentiable advection leveraging the Navier-Stokes equations is used to estimate fine-grained density and velocity fields, supporting re-simulation and downstream tasks. The method claims superior reconstruction quality and efficiency relative to prior state-of-the-art approaches.

Significance. If the empirical results hold, the work would constitute a meaningful contribution to single-view fluid reconstruction in computer graphics by demonstrating how diffusion-based generative models, when conditioned on physical velocity constraints and embedded in a progressive refinement pipeline, can mitigate the severe ill-posedness of the problem while preserving physical consistency for re-simulation. The explicit combination of generative synthesis with differentiable physics is a timely and practical direction.

major comments (1)
  1. [Abstract / Method description of side-view synthesizer] The central claim that the side-view synthesizer 'significantly alleviates the ill-posedness' (abstract) rests on the assumption that velocity-field-conditioned diffusion outputs are sufficiently spatio-temporally consistent. No quantitative evaluation of this consistency (e.g., temporal coherence metrics, optical-flow consistency scores, or ablation on the velocity conditioning) is referenced in the provided description; without such evidence the downstream reconstruction quality gains cannot be attributed to this component.
minor comments (2)
  1. [Method] The progressive multi-stage refinement process is described at a high level; a diagram or pseudocode outlining the exact sequence of view-angle increments and refinement iterations would improve clarity.
  2. [Experiments] Comparison to prior work would benefit from explicit citation of the specific baselines used for both reconstruction quality and runtime measurements.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and recommendation for minor revision. We address the major comment on the side-view synthesizer below.

read point-by-point responses

  1. Referee: [Abstract / Method description of side-view synthesizer] The central claim that the side-view synthesizer 'significantly alleviates the ill-posedness' (abstract) rests on the assumption that velocity-field-conditioned diffusion outputs are sufficiently spatio-temporally consistent. No quantitative evaluation of this consistency (e.g., temporal coherence metrics, optical-flow consistency scores, or ablation on the velocity conditioning) is referenced in the provided description; without such evidence the downstream reconstruction quality gains cannot be attributed to this component.

    Authors: We appreciate the referee highlighting the need for quantitative support. The current manuscript demonstrates spatio-temporal consistency primarily through qualitative visual results and the downstream improvements in reconstruction quality and re-simulation. We agree that explicit metrics would strengthen attribution to the velocity conditioning. In the revised version we will add temporal coherence and optical-flow consistency scores together with an ablation isolating the velocity-field constraint. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's pipeline begins with a diffusion-based side-view synthesizer that incorporates velocity field constraints drawn from external Navier-Stokes physics, then proceeds to progressive multi-stage novel-view refinement and final density/velocity estimation via differentiable advection. No step reduces a claimed prediction or first-principles result to a fitted parameter or self-referential definition by construction. The abstract and method outline treat the physical constraints and diffusion priors as independent inputs rather than outputs of the reconstruction itself. No load-bearing self-citations or ansatz smuggling are described. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no specific free parameters, axioms, or invented entities can be extracted or verified from the provided text.

pith-pipeline@v0.9.0 · 5758 in / 1085 out tokens · 21058 ms · 2026-05-19T04:40:24.152408+00:00 · methodology

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 1 internal anchor

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