14 research outputs found
Lagrangian Neural Style Transfer for Fluids
Artistically controlling the shape, motion and appearance of fluid
simulations pose major challenges in visual effects production. In this paper,
we present a neural style transfer approach from images to 3D fluids formulated
in a Lagrangian viewpoint. Using particles for style transfer has unique
benefits compared to grid-based techniques. Attributes are stored on the
particles and hence are trivially transported by the particle motion. This
intrinsically ensures temporal consistency of the optimized stylized structure
and notably improves the resulting quality. Simultaneously, the expensive,
recursive alignment of stylization velocity fields of grid approaches is
unnecessary, reducing the computation time to less than an hour and rendering
neural flow stylization practical in production settings. Moreover, the
Lagrangian representation improves artistic control as it allows for
multi-fluid stylization and consistent color transfer from images, and the
generality of the method enables stylization of smoke and liquids likewise.Comment: ACM Transaction on Graphics (SIGGRAPH 2020), additional materials:
http://www.byungsoo.me/project/lnst/index.htm
Surface Simplification using Intrinsic Error Metrics
This paper describes a method for fast simplification of surface meshes.
Whereas past methods focus on visual appearance, our goal is to solve equations
on the surface. Hence, rather than approximate the extrinsic geometry, we
construct a coarse intrinsic triangulation of the input domain. In the spirit
of the quadric error metric (QEM), we perform greedy decimation while
agglomerating global information about approximation error. In lieu of
extrinsic quadrics, however, we store intrinsic tangent vectors that track how
far curvature "drifts" during simplification. This process also yields a
bijective map between the fine and coarse mesh, and prolongation operators for
both scalar- and vector-valued data. Moreover, we obtain hard guarantees on
element quality via intrinsic retriangulation - a feature unique to the
intrinsic setting. The overall payoff is a "black box" approach to geometry
processing, which decouples mesh resolution from the size of matrices used to
solve equations. We show how our method benefits several fundamental tasks,
including geometric multigrid, all-pairs geodesic distance, mean curvature
flow, geodesic Voronoi diagrams, and the discrete exponential map.Comment: SIGGRAPH 202
Spectral Mesh Simplification
International audienceThe spectrum of the Laplace-Beltrami operator is instrumental for a number of geometric modeling applications, from processing to analysis. Recently, multiple methods were developed to retrieve an approximation of a shape that preserves its eigenvectors as much as possible, but these techniques output a subset of input points with no connectivity, which limits their potential applications. Furthermore, the obtained Laplacian results from an optimization procedure, implying its storage alongside the selected points. Focusing on keeping a mesh instead of an operator would allow to retrieve the latter using the standard cotangent formulation, enabling easier processing afterwards. Instead, we propose to simplify the input mesh using a spectrum-preserving mesh decimation scheme, so that the Laplacian computed on the simplified mesh is spectrally close to the one of the input mesh. We illustrate the benefit of our approach for quickly approximating spectral distances and functional maps on low resolution proxies of potentially high resolution input meshes