SCA 17: Eurographics/SIGGRAPH Symposium on Computer Animation

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https://diglib.eg.org/handle/10.2312/2631998

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Browsing SCA 17: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "Computing methodologies Animation"

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    Authoring Motion Cycles
    (ACM, 2017) Ciccone, Loïc; Guay, Martin; Nitti, Maurizio; Sumner, Robert W.; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    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 explores the speci c challenges of motion cycle authoring and provides a system simple enough for novice animators while maintaining the flexibility of control demanded by experts. Due to their cyclic nature, we show that performance animation provides a natural interface for motion cycle speci cation. Our system allows the user to act several loops of motion using a variety of capture devices and automatically extracts a looping cycle from this potentially noisy input. Motion cycles for di erent character components can be authored in a layered fashion, or our method supports cycle extraction from higher-dimensional data for capture devices that deliver many degrees of freedom. After capture, a custom curve representation and manipulation tool allows the user to coordinate and control spatial and temporal transformations from a single viewport. Ground and other planar contacts are speci ed with a single sketched line that adjusts a curve's position and timing to establish non-slipping contact. We evaluate the e ectiveness of our work through tests with both novice and expert users and show a variety of animated motion cycles created with our system.
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    Fully Asynchronous SPH Simulation
    (ACM, 2017) Reinhardt, Stefan; Huber, Markus; Eberhardt, Bernhard; Weiskopf, Daniel; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    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 able to increase the e ciency of simulations. Previous approaches of locally adaptive time steps have shown promising results in the form of increased time steps, however, they need to synchronize time steps in recurring intervals, which involves either interpolation operations or matching time steps. With our method, time steps are asynchronous through the whole simulation and no global time barriers are needed. In addition, we present an e cient method for parallelization of our novel asynchronous time integration. For both serial and parallel execution, we achieve speedups of up to 7:5 compared to fixed time steps and are able to outperform previous adaptive approaches considerably
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    Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?
    (ACM, 2017) Peng, Xue Bin; Panne, Michiel van de; Bernhard Thomaszewski and KangKang Yin and Rahul Narain
    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 of four di erent action parameterizations (torques, muscle-activations, target joint angles, and target jointangle velocities) in terms of learning time, policy robustness, motion quality, and policy query rates. Our results are evaluated on a gaitcycle imitation task for multiple planar articulated figures and multiple gaits. We demonstrate that the local feedback provided by higher-level action parameterizations can signi cantly impact the learning, robustness, and motion quality of the resulting policies.