SCA 19: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/2632835

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Browsing SCA 19: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "deformable bodies"

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    Fast Simulation of Deformable Characters with Articulated Skeletons in Projective Dynamics
    (ACM, 2019) Li, Jing; Liu, Tiantian; Kavan, Ladislav; Batty, Christopher and Huang, Jin
    We propose a fast and robust solver to simulate continuum-based deformable models with constraints, in particular, rigid-body and joint constraints useful for soft articulated characters. Our method embeds degrees of freedom of both articulated rigid bodies and deformable bodies in one unified optimization problem, thus coupling the deformable and rigid bodies. Our method can efficiently simulate character models, with rigid-body parts (bones) being correctly coupled with deformable parts (flesh). Our method is stable because backward Euler time integration is applied to rigid as well as deformable degrees of freedom. Our method is rigorously derived from constrained Newtonian mechanics. In an example simulation with rigid bodies only, we demonstrate that our method converges to the same motion as classical explicitly integrated rigid body simulator.
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    GPU-Based Contact-Aware Trajectory Optimization Using A Smooth Force Model
    (ACM, 2019) Pan, Zherong; Ren, Bo; Manocha, Dinesh; Batty, Christopher and Huang, Jin
    We present a new formulation of trajectory optimization for articulated bodies. Our approach uses a fully differentiable dynamic model of the articulated body, and a smooth force model that approximates all kinds of internal/external forces as a smooth function of the articulated body's kinematic state. Our formulation is contact-aware and its complexity is not dependent on the contact positions or the number of contacts. Furthermore, we exploit the block-tridiagonal structure of the Hessian matrix and present a highly parallel Newton-type trajectory optimizer that maps well to GPU architectures. Moreover, we use a Markovian regularization term to overcome the local minima problems in the optimization formulation. We highlight the performance of our approach using a set of locomotion tasks performed by characters with 15 − 35 DOFs. In practice, our GPU-based algorithm running on a NVIDIA TITAN-X GPU provides more than 30× speedup over a multi-core CPU-based implementation running on Intel Xeon E5-1620 CPU. In addition, we demonstrate applications of our method on various applications such as contact-rich motion planning, receding-horizon control, and motion graph construction.