8 research outputs found
Neural Smoke Stylization with Color Transfer
Artistically controlling fluid simulations requires a large amount of manual
work by an artist. The recently presented transportbased neural style transfer
approach simplifies workflows as it transfers the style of arbitrary input
images onto 3D smoke simulations. However, the method only modifies the shape
of the fluid but omits color information. In this work, we therefore extend the
previous approach to obtain a complete pipeline for transferring shape and
color information onto 2D and 3D smoke simulations with neural networks. Our
results demonstrate that our method successfully transfers colored style
features consistently in space and time to smoke data for different input
textures.Comment: Submitted to Eurographics202
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
Editing smoke animation using a deforming grid
Abstract We present a new method for editing smoke animations by directly deforming the grid used for simulation. We present a modification to the widely used semi-Lagrangian advection operator and use it to transfer the deformation from the grid to the smoke body. Our modified operator bends the smoke particle streamlines according to the deformation gradient. We demonstrate that the controlled smoke animation preserves the fine-grained vortical velocity components and incompressibility constraints, while conforming to the deformed grid. Moreover, our approach enables interactive 3D smoke animation editing by using a reduced-dimensional subspace. Overall, our method makes it possible to use current mesh editing tools to control the smoke body
Tools for fluid simulation control in computer graphics
L’animation basée sur la physique peut générer des systèmes aux comportements complexes
et réalistes. Malheureusement, contrôler de tels systèmes est une tâche ardue. Dans le cas
de la simulation de fluide, le processus de contrôle est particulièrement complexe. Bien
que de nombreuses méthodes et outils ont été mis au point pour simuler et faire le rendu
de fluides, trop peu de méthodes offrent un contrôle efficace et intuitif sur une simulation
de fluide. Étant donné que le coût associé au contrôle vient souvent s’additionner au coût
de la simulation, appliquer un contrôle sur une simulation à plus haute résolution rallonge
chaque itération du processus de création. Afin d’accélérer ce processus, l’édition peut se
faire sur une simulation basse résolution moins coûteuse. Nous pouvons donc considérer que
la création d’un fluide contrôlé peut se diviser en deux phases: une phase de contrôle durant
laquelle un artiste modifie le comportement d’une simulation basse résolution, et une phase
d’augmentation de détail durant laquelle une version haute résolution de cette simulation
est gĂ©nĂ©rĂ©e. Cette thèse prĂ©sente deux projets, chacun contribuant Ă l’état de l’art reliĂ© Ă
chacune de ces deux phases.
Dans un premier temps, on introduit un nouveau système de contrôle de liquide représenté
par un modèle particulaire. À l’aide de ce système, un artiste peut sélectionner dans une base
de données une parcelle de liquide animé précalculée. Cette parcelle peut ensuite être placée
dans une simulation afin d’en modifier son comportement. À chaque pas de simulation, notre
système utilise la liste de parcelles actives afin de reproduire localement la vision de l’artiste.
Une interface graphique intuitive a été développée, inspirée par les logiciels de montage vidéo,
et permettant Ă un utilisateur non expert de simplement Ă©diter une simulation de liquide.
Dans un second temps, une méthode d’augmentation de détail est décrite. Nous proposons
d’ajouter une étape supplémentaire de suivi après l’étape de projection du champ de
vitesse d’une simulation de fumée eulérienne classique. Durant cette étape, un champ de
perturbations de vitesse non-divergent est calculé, résultant en une meilleure correspondance
des densités à haute et à basse résolution. L’animation de fumée résultante reproduit fidèlement
l’aspect grossier de la simulation d’entrée, tout en étant augmentée à l’aide de détails
simulés.Physics-based animation can generate dynamic systems of very complex and realistic behaviors.
Unfortunately, controlling them is a daunting task. In particular, fluid simulation
brings up particularly difficult problems to the control process. Although many methods
and tools have been developed to convincingly simulate and render fluids, too few methods
provide efficient and intuitive control over a simulation. Since control often comes with extra
computations on top of the simulation cost, art-directing a high-resolution simulation leads
to long iterations of the creative process. In order to shorten this process, editing could be
performed on a faster, low-resolution model. Therefore, we can consider that the process of
generating an art-directed fluid could be split into two stages: a control stage during which
an artist modifies the behavior of a low-resolution simulation, and an upresolution stage
during which a final high-resolution version of this simulation is driven. This thesis presents
two projects, each one improving on the state of the art related to each of these two stages.
First, we introduce a new particle-based liquid control system. Using this system, an
artist selects patches of precomputed liquid animations from a database, and places them in
a simulation to modify its behavior. At each simulation time step, our system uses these entities
to control the simulation in order to reproduce the artist’s vision. An intuitive graphical
user interface inspired by video editing tools has been developed, allowing a nontechnical
user to simply edit a liquid animation.
Second, a tracking solution for smoke upresolution is described. We propose to add an
extra tracking step after the projection of a classical Eulerian smoke simulation. During
this step, we solve for a divergence-free velocity perturbation field resulting in a better
matching of the low-frequency density distribution between the low-resolution guide and the
high-resolution simulation. The resulting smoke animation faithfully reproduces the coarse
aspect of the low-resolution input, while being enhanced with simulated small-scale details
Accelerating ADMM for efficient simulation and optimization
The alternating direction method of multipliers (ADMM) is a popular approach for solving optimization problems that are potentially non-smooth and with hard constraints. It has been applied to various computer graphics applications, including physical simulation, geometry processing, and image processing. However, ADMM can take a long time to converge to a solution of high accuracy. Moreover, many computer graphics tasks involve non-convex optimization, and there is often no convergence guarantee for ADMM on such problems since it was originally designed for convex optimization. In this paper, we propose a method to speed up ADMM using Anderson acceleration, an established technique for accelerating fixed-point iterations. We show that in the general case, ADMM is a fixed-point iteration of the second primal variable and the dual variable, and Anderson acceleration can be directly applied. Additionally, when the problem has a separable target function and satisfies certain conditions, ADMM becomes a fixed-point iteration of only one variable, which further reduces the computational overhead of Anderson acceleration. Moreover, we analyze a particular non-convex problem structure that is common in computer graphics, and prove the convergence of ADMM on such problems under mild assumptions. We apply our acceleration technique on a variety of optimization problems in computer graphics, with notable improvement on their convergence speed
Efficient Motion Planning for Deformable Objects with High Degrees of Freedom
Many robotics and graphics applications need to be able to plan motions by interacting with complex environmental objects, including solids, sands, plants, and fluids. A key aspect of these deformable objects is that they have high-DOF, which implies that they can move or change shapes in many independent ways subject to physics-based constraints. In these applications, users also impose high-level goals on the movements of high-DOF objects, and planning algorithms need to model their motions and determine the optimal control actions to satisfy the high-level goals. In this thesis, we propose several planning algorithms for high-DOF objects. Our algorithms can improve the scalability considerably and can plan motions for different types of objects, including elastically deformable objects, free-surface flows, and Eulerian fluids. We show that the salient deformations of elastically deformable objects lie in a low-dimensional nonlinear space, i.e., the RS space. By embedding the configuration space in the RS subspace, our optimization-based motion planning algorithm can achieve over two orders of magnitude speedup over prior optimization-based formulations. For free surface flows such as liquids, we utilize features of the planning problems and machine learning techniques to identify low-dimensional latent spaces to accelerate the motion planning computation. For Eulerian fluids without free surfaces, we present a scalable planning algorithm based on novel numerical techniques. We show that the numerical discretization scheme exhibits strong regularity, which allows us to accelerate optimization-based motion planning algorithms using a hierarchical data structure and we can achieve 3-10 times speedup over gradient-based optimization techniques. Finally, for high-DOF objects with many frictional contacts with the environment, we present a contact dynamic model that can handle contacts without expensive combinatorial optimization. We illustrate the benefits of our high-DOF planning algorithms for three applications. First, we can plan contact-rich motion trajectories for general elastically deformable robots. Second, we can achieve real-time performance in terms of planning the motion of a robot arm to transfer the liquids between containers. Finally, our method enables a more intuitive user interface. We allow animation editors to modify animations using an offline motion planner to generate controlled fluid animations.Doctor of Philosoph