DualSmoke: Sketch-based smoke illustration design with two-stage generative model

Abstract

The dynamic effects of smoke are impressive in illustration design, but it is a troublesome and challenging issue for inexpert users to design smoke effects without domain knowledge of fluid simulations. In this work, we propose DualSmoke, a two-stage global-to-local generation framework for interactive smoke illustration design. In the global stage, the proposed approach utilizes fluid patterns to generate Lagrangian coherent structures from the user’s hand-drawn sketches. In the local stage, detailed flow patterns are obtained from the generated coherent structure. Finally, we apply a guiding force field to the smoke simulator to produce the desired smoke illustration. To construct the training dataset, DualSmoke generates flow patterns using finite-time Lyapunov exponents of the velocity fields. The synthetic sketch data are generated from the flow patterns by skeleton extraction. Our user study verifies that the proposed design interface can provide various smoke illustration designs with good user usability. Our code is available at https://githubcom/shasph/DualSmoke.