Now showing items 1-17 of 17

    • Designing Cable-Driven Actuation Networks for Kinematic Chains and Trees 

      Megaro, Vittorio; Knoop, Espen; Spielberg, Andrew; Levin, David I.W.; Matusik, Wojciech; Gross, Markus; Thomaszewski, Bernhard; Bächer, Moritz (ACM, 2017)
      In this paper we present an optimization-based approach for the design of cable-driven kinematic chains and trees. Our system takes as input a hierarchical assembly consisting of rigid links jointed together with hinges. ...
    • Evaporation and Condensation of SPH-based Fluids 

      Hochstetter, Hendrik; Kolb, Andreas (ACM, 2017)
      In this paper we present a method to simulate evaporation and condensation of liquids. Therefore, both the air and liquid phases have to be simulated. We use, as a carrier of vapor, a coarse grid for the air phase and ...
    • A Positive-Definite Cut-Cell Method for Strong Two-Way Coupling Between Fluids and Deformable Bodies 

      Zarifi, Omar; Batty, Christopher (ACM, 2017)
      We present a new approach to simulation of two-way coupling between inviscid free surface fluids and deformable bodies that exhibits several notable advantages over previous techniques. By fully incorporating the dynamics ...
    • Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks 

      Laine, Samuli; Karras, Tero; Aila, Timo; Herva, Antti; Saito, Shunsuke; Yu, Ronald; Li, Hao; Lehtinen, Jaakko (ACM, 2017)
      We present a real-time deep learning framework for video-based facial performance capture-the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a ...
    • A Micropolar Material Model for Turbulent SPH Fluids 

      Bender, Jan; Koschier, Dan; Kugelstadt, Tassilo; Weiler, Marcel (ACM, 2017)
      In this paper we introduce a novel micropolar material model for the simulation of turbulent inviscid fluids. The governing equations are solved by using the concept of Smoothed Particle Hydrodynamics (SPH). As already ...
    • Rigid Body Contact Problems using Proximal Operators 

      Erleben, Kenny (ACM, 2017)
      Iterative methods are popular for solving contact force problems in rigid body dynamics. They are loved for their robustness and surrounded by mystery as to whether they converge or not. We provide a mathematical foundation ...
    • Authoring Motion Cycles 

      Ciccone, Loïc; Guay, Martin; Nitti, Maurizio; Sumner, Robert W. (ACM, 2017)
      Motion cycles play an important role in animation production and game development. However, creating motion cycles relies on general-purpose animation packages with complex interfaces that require expert training. Our work ...
    • Density Maps for Improved SPH Boundary Handling 

      Koschier, Dan; Bender, Jan (ACM, 2017)
      In this paper, we present the novel concept of density maps for robust handling of static and rigid dynamic boundaries in fluid simulations based on Smoothed Particle Hydrodynamics (SPH). In contrast to the vast majority ...
    • Inequality Cloth 

      Jin, Ning; Lu, Wenlong; Geng, Zhenglin; Fedkiw, Ronald P. (ACM, 2017)
      As has been noted and discussed by various authors, numerical simulations of deformable bodies often adversely suffer from so-called ''locking'' artifacts. We illustrate that the ''locking'' of out-of-plane bending motion ...
    • Physically-Based Droplet Interaction 

      Jones, Richard; Southern, Richard (ACM, 2017)
      In this paper we present a physically-based model for simulating realistic interactions between liquid droplets in an e cient manner. Our particle-based system recreates the coalescence, separation and fragmentation ...
    • Augmenting Sampling Based Controllers with Machine Learning 

      Rajamäki, Joose; Hämäläinen, Perttu (ACM, 2017)
      E cient learning of 3D character control still remains an open problem despite of the remarkable recent advances in the field. We propose a new algorithm that combines planning by a samplingbased model-predictive controller ...
    • Long Range Constraints for Rigid Body Simulations 

      Müller, Matthias; Chentanez, Nuttapong; Macklin, Miles; Jeschke, Stefan (ACM, 2017)
      The two main constraints used in rigid body simulations are contacts and joints. Both constrain the motion of a small number of bodies in close proximity. However, it is often the case that a series of constraints restrict ...
    • Hierarchical Vorticity Skeletons 

      Eberhardt, Sebastian; Weissmann, Steffen; Pinkall, Ulrich; Thuerey, Nils (ACM, 2017)
      We propose a novel method to extract hierarchies of vortex filaments from given three-dimensional flow velocity fields. We call these collections of filaments Hierarchical Vorticity Skeletons (HVS). They extract multi-scale ...
    • Fully Asynchronous SPH Simulation 

      Reinhardt, Stefan; Huber, Markus; Eberhardt, Bernhard; Weiskopf, Daniel (ACM, 2017)
      We present a novel method for fully asynchronous time integration of particle-based fluids using smoothed particle hydrodynamics (SPH). With our approach, we allow a dedicated time step for each particle. Therefore, we are ...
    • Emotion Control of Unstructured Dance Movements 

      Aristidou, Andreas; Zeng, Qiong; Stavrakis, Efstathios; Yin, KangKang; Cohen-Or, Daniel; Chrysanthou, Yiorgos; Chen, Baoquan (ACM, 2017)
      Motion capture technology has enabled the acquisition of high quality human motions for animating digital characters with extremely high fidelity. However, despite all the advances in motion editing and synthesis, it remains ...
    • Modeling and Data-Driven Parameter Estimation for Woven Fabrics 

      Clyde, David; Teran, Joseph; Tamstorf, Rasmus (ACM, 2017)
      Accurate estimation of mechanical parameters for simulation of woven fabrics is essential in many fields. To facilitate this we first present a new orthotropic hyperelastic constitutive model for woven fabrics. Next, we ...
    • Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter? 

      Peng, Xue Bin; Panne, Michiel van de (ACM, 2017)
      The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts learning and the resulting performance. We compare the impact ...