VortMixture: Vortex-Driven Dynamic Smoke Reconstruction with Local Gaussian Mixtures

Reconstructing dynamic smoke from sparse multi-view observations is challenging due to its semi-transparent appearance, complex vortical motion, and severe ambiguity in recovering time-varying volumetric structure. Existing approaches commonly incorporate fluid priors as additional objectives alongside visual reconstruction losses, requiring heterogeneous physical and visual constraints to be jointly balanced. We propose VortMixture, a dynamic smoke reconstruction framework that embeds fluid structure directly into the motion representation while retaining flexible visual modeling. Specifically, we parameterize the Eulerian velocity field with vortex kernels, yielding divergence-free motion by construction and spatially coupling localized vorticity with the surrounding flow. The resulting field advects Lagrangian Gaussian primitives, while propagated vortex strengths model vortical evolution over time. To capture local variations beyond advection, we further associate each transported primitive with a local Gaussian mixture, increasing the capacity for density and appearance modeling without altering its underlying carrier trajectory. Experiments across three real-world smoke datasets demonstrate consistent improvements in sparse-view smoke reconstruction quality over prior methods.