A Rapid, End‐to‐end, Generative Model for Gaseous Phenomena from Limited Views

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dc.contributor.authorQiu, Shengen_US
dc.contributor.authorLi, Chenen_US
dc.contributor.authorWang, Changboen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2021-10-08T07:38:13Z
dc.date.available2021-10-08T07:38:13Z
dc.date.issued2021
dc.description.abstractDespite the rapid development and proliferation of computer graphics hardware devices for scene capture in the most recent decade, the high‐resolution 3D/4D acquisition of gaseous scenes (e.g., smokes) in real time remains technically challenging in graphics research nowadays. In this paper, we explore a hybrid approach to simultaneously taking advantage of both the model‐centric method and the data‐driven method. Specifically, this paper develops a novel conditional generative model to rapidly reconstruct the temporal density and velocity fields of gaseous phenomena based on the sequence of two projection views. With the data‐driven method, we can achieve the strong coupling of density update and the estimation of flow motion, as a result, we can greatly improve the reconstruction performance for smoke scenes. First, we employ a conditional generative network to generate the initial density field from input projection views and estimate the flow motion based on the adjacent frames. Second, we utilize the differentiable advection layer and design a velocity estimation network with the long‐term mechanism to help achieve the end‐to‐end training and more stable graphics effects. Third, we can re‐simulate the input scene with flexible coupling effects based on the estimated velocity field subject to artists' guidance or user interaction. Moreover, our generative model could accommodate single projection view as input. In practice, more input projection views are enabling and facilitating the high‐fidelity reconstruction with more realistic and finer details. We have conducted extensive experiments to confirm the effectiveness, efficiency, and robustness of our new method compared with the previous state‐of‐the‐art techniques.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14270
dc.identifier.issn1467-8659
dc.identifier.pages242-257
dc.identifier.urihttps://doi.org/10.1111/cgf.14270
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14270
dc.publisher© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectphysically based animation
dc.subjectfluid reconstruction
dc.subjectanimation
dc.subjectsurface reconstruction
dc.subjectmodelling
dc.titleA Rapid, End‐to‐end, Generative Model for Gaseous Phenomena from Limited Viewsen_US
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