Browsing by Author "Tang, Jingwei"

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    Deep Reconstruction of 3D Smoke Densities from Artist Sketches
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Kim, Byungsoo; Huang, Xingchang; Wuelfroth, Laura; Tang, Jingwei; Cordonnier, Guillaume; Gross, Markus; Solenthaler, Barbara; Chaine, Raphaƫlle; Kim, Min H.
    Creative processes of artists often start with hand-drawn sketches illustrating an object. Pre-visualizing these keyframes is especially challenging when applied to volumetric materials such as smoke. The authored 3D density volumes must capture realistic flow details and turbulent structures, which is highly non-trivial and remains a manual and time-consuming process. We therefore present a method to compute a 3D smoke density field directly from 2D artist sketches, bridging the gap between early-stage prototyping of smoke keyframes and pre-visualization. From the sketch inputs, we compute an initial volume estimate and optimize the density iteratively with an updater CNN. Our differentiable sketcher is embedded into the end-to-end training, which results in robust reconstructions. Our training data set and sketch augmentation strategy are designed such that it enables general applicability. We evaluate the method on synthetic inputs and sketches from artists depicting both realistic smoke volumes and highly non-physical smoke shapes. The high computational performance and robustness of our method at test time allows interactive authoring sessions of volumetric density fields for rapid prototyping of ideas by novice users.
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    Physics-Informed Neural Corrector for Deformation-based Fluid Control
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Tang, Jingwei; Kim, Byungsoo; Azevedo, Vinicius C.; Solenthaler, Barbara; Myszkowski, Karol; Niessner, Matthias
    Controlling fluid simulations is notoriously difficult due to its high computational cost and the fact that user control inputs can cause unphysical motion. We present an interactive method for deformation-based fluid control. Our method aims at balancing the direct deformations of fluid fields and the preservation of physical characteristics. We train convolutional neural networks with physics-inspired loss functions together with a differentiable fluid simulator, and provide an efficient workflow for flow manipulations at test time. We demonstrate diverse test cases to analyze our carefully designed objectives and show that they lead to physical and eventually visually appealing modifications on edited fluid data.