DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Model

Haoran Xie; Keisuke Arihara; Syuhei Sato; Kazunori Miyata

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arXiv · 2208.10906

DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Model

Haoran Xie·Keisuke Arihara·Syuhei Sato·Kazunori Miyata

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Abstract

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

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Haoran Xie, Keisuke Arihara, Syuhei Sato, Kazunori Miyata. 2022-08-23. DualSmoke: Sketch-Based Smoke Illustration Design with Two-Stage Generative Model. https://arxiv.org/abs/2208.10906

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