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.

翻译：从稀疏视角重建动态流体是一个长期且具有挑战性的问题，其原因在于不充分的视角覆盖导致三维信息的严重缺失。尽管已有若干开创性方法尝试利用可微渲染或新视角合成技术解决该问题，但这些方法往往受限于病态条件下耗时的优化过程。我们提出SmokeSVD——一种高效且有效的框架，通过将扩散模型的生成能力与物理引导的一致性优化相结合，从单段视频中渐进式地重建动态烟雾。具体而言，我们首先提出一种基于扩散模型的物理引导侧视角合成器，该合成器显式融入速度场约束，逐帧生成时空一致的侧视角图像，显著缓解了单视角重建的病态性。随后，我们通过渐进式多阶段过程迭代精化新视角图像并重建三维密度场：从不断增加的视角角度渲染并增强图像，从而生成高质量的多视角序列。最终，我们利用纳维-斯托克斯方程，通过可微平流估计细粒度的密度场与速度场。我们的方法支持重仿真与下游应用，同时在重建质量与计算效率上均优于现有最优方法。