VolSmoke: Reconstructing Smoke from a Single View with Density-Space Priors

Recovering time-varying 3D smoke density and velocity from monocular video would enable accessible volumetric capture and physics-based re-simulation, but the problem is severely underconstrained: each pixel constrains only a ray integral, leaving the density distribution in space ambiguous. Recent methods address this by generating novel views, yet such image-space priors need not correspond to a consistent 3D volume, which can hinder subsequent physical reconstruction. We instead resolve the missing information directly in 3D density space, where a single volume provides a shared explanation across views and a physical state for transport. We introduce VolSmoke, a monocular reconstruction framework built around a view-conditioned latent prior over 3D smoke density. At inference, we sample plausible densities from this prior and use render-guided reverse diffusion to align each sample with the observed view. Temporal coordination further lets neighboring frames jointly constrain the otherwise ambiguous density. The resulting density sequence is then jointly refined with inflow source and a continuously divergence-free velocity field under rendering and transport constraints. On the real-world ScalarFlow dataset and a challenging synthetic Plume benchmark, VolSmoke substantially improves held-out-view reconstruction and velocity recovery over state-of-the-art novel-view baselines, leading to more accurate re-simulation. Our density prior can be trained from either synthetic simulations or simple tomographic reconstructions from sparse-view real-world captures, without greater data requirements than prior image-space approaches. These results show that density-space completion improves not only appearance reconstruction, but also the physical state required for velocity estimation and re-simulation.