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

Despite 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.