3D SMOKE UPSCALING USING LIGHTWEIGHT DNN

This paper presents a hybrid approach for accelerating 3D smoke simulations in computer graphics using a lightweight deep neural network for volumetric upscaling. High-fidelity smoke simulation is computationally intensive due to the cubic growth of spatial resolution and the cost of solving the Navier-Stokes equations. Our method reduces computational demand by performing simulation at a lower resolution and subsequently reconstructing high-resolution volumetric fields using a compact neural network architecture. The proposed model focuses on recovering fine-scale turbulent structures and visual complexity while preserving the large-scale physical consistency provided by the underlying solver. Experimental results indicate substantial computational speedups relative to native high-resolution simulation, while qualitative comparisons suggest plausible recovery of fine-scale smoke detail. The proposed framework therefore targets interactive or near-real-time smoke preview workflows on commodity hardware, improving accessibility for iterative graphics production.