Browsing by Author "Paquette, Eric"
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Item Distribution Update of Deformable Patches for Texture Synthesis on the Free Surface of Fluids(The Eurographics Association and John Wiley & Sons Ltd., 2019) Gagnon, jonathan; Guzmán, Julián E.; Vervondel, Valentin; Dagenais, François; Mould, David; Paquette, Eric; Lee, Jehee and Theobalt, Christian and Wetzstein, GordonWe propose an approach for temporally coherent patch-based texture synthesis on the free surface of fluids. Our approach is applied as a post-process, using the surface and velocity field from any fluid simulator. We apply the texture from the exemplar through multiple local mesh patches fitted to the surface and mapped to the exemplar. Our patches are constructed from the fluid free surface by taking a subsection of the free surface mesh. As such, they are initially very well adapted to the fluid's surface, and can later deform according to the free surface velocity field, allowing a greater ability to represent surface motion than rigid or 2D grid-based patches. From one frame to the next, the patch centers and surrounding patch vertices are advected according to the velocity field. We seek to maintain a Poisson disk distribution of patches, and following advection, the Poisson disk criterion determines where to add new patches and which patches should e flagged for removal. The removal considers the local number of patches: in regions containing too many patches, we accelerate the temporal removal. This reduces the number of patches while still meeting the Poisson disk criterion. Reducing areas with too many patches speeds up the computation and avoids patch-blending artifacts. The final step of our approach creates the overall texture in an atlas where each texel is computed from the patches using a contrast-preserving blending function. Our tests show that the approach works well on free surfaces undergoing significant deformation and topological changes. Furthermore, we show that our approach provides good results for many fluid simulation scenarios, and with many texture exemplars. We also confirm that the optical flow from the resulting texture matches the fluid velocity field. Overall, our approach compares favorably against recent work in this area.Item EUROGRAPHICS 2022: Education Papers Frontmatter(The Eurographics Association, 2022) Bourdin, Jean-Jacques; Paquette, Eric; Bourdin, Jean-Jacques; Paquette, EricItem Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids(ACM, 2021) Roy, Bruno; Poulin, Pierre; Paquette, Eric; Narain, Rahul and Neff, Michael and Zordan, VictorWe present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation. The proposed network leverages neighborhood contributions to encode inherent liquid properties throughout convolutions. We also propose a particle-based approach to interpolate between liquids generated from varying simulation discretizations using a state-of-the-art bidirectional optical flow solver method for fluids in addition with a novel key-event topological alignment constraint. In conjunction with the neighborhood contributions, our loss formulation allows the inference model throughout epochs to reward important differences in regard to significant gaps in simulation discretizations. Even when applied in an untested simulation setup, our approach is able to generate plausible high-resolution details. Using this interpolation approach and the predicted displacements, our approach combines the input liquid properties with the predicted motion to infer semi-Lagrangian advection. We furthermore showcase how the proposed interpolation approach can facilitate generating large simulation datasets with a subset of initial condition parameters.