Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Solenthaler, Barbara"'
Autor:
Yang, Lingchen, Zoss, Gaspard, Chandran, Prashanth, Gross, Markus, Solenthaler, Barbara, Sifakis, Eftychios, Bradley, Derek
Physically-based simulation is a powerful approach for 3D facial animation as the resulting deformations are governed by physical constraints, allowing to easily resolve self-collisions, respond to external forces and perform realistic anatomy edits.
Externí odkaz:
http://arxiv.org/abs/2402.19477
Autor:
Yang, Lingchen, Zoss, Gaspard, Chandran, Prashanth, Gotardo, Paulo, Gross, Markus, Solenthaler, Barbara, Sifakis, Eftychios, Bradley, Derek
3D facial animation is often produced by manipulating facial deformation models (or rigs), that are traditionally parameterized by expression controls. A key component that is usually overlooked is expression 'style', as in, how a particular expressi
Externí odkaz:
http://arxiv.org/abs/2401.15414
Autor:
Yang, Lingchen, Kim, Byungsoo, Zoss, Gaspard, Gözcü, Baran, Gross, Markus, Solenthaler, Barbara
Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation. Similar to recent work, this paper utilizes a differentiable, quasi-static, and physics-based simulation layer to optimize for actuation sig
Externí odkaz:
http://arxiv.org/abs/2401.14861
We present a quasi-static finite element simulator for human face animation. We model the face as an actuated soft body, which can be efficiently simulated using Projective Dynamics (PD). We adopt Incremental Potential Contact (IPC) to handle self-in
Externí odkaz:
http://arxiv.org/abs/2312.02999
We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corr
Externí odkaz:
http://arxiv.org/abs/2302.14470
We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition w
Externí odkaz:
http://arxiv.org/abs/2104.06031
Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization
Externí odkaz:
http://arxiv.org/abs/2009.06944
Publikováno v:
ACM Trans. Graph. 39, 4, Article 1 (July 2020), 10 pages
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 viewpoin
Externí odkaz:
http://arxiv.org/abs/2005.00803
We propose an end-to-end trained neural networkarchitecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke simulations in 2D and 3D based on the incompressible Navier-Stokes (NS) e
Externí odkaz:
http://arxiv.org/abs/2003.08723
Publikováno v:
Eurographics 2020 - Short Papers
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 smok
Externí odkaz:
http://arxiv.org/abs/1912.08757