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Human motions are the product of internal and external forces, but these forces are very difficult to measure in a general setting. Given a motion capture trajectory, we propose a method to reconstruct its open-loop control and the implicit contact forces. The method employs a strategy based on randomized sampling of the control within user-specified(More)
We present a data-driven method for deformation capture and modeling of general soft objects. We adopt an iterative framework that consists of one component for physics-based deformation tracking and another for spacetime optimization of deformation parameters. Low cost depth sensors are used for the deformation capture, and we do not require any(More)
A key enabling technology of NFV is software dataplane, which has attracted much attention in both academia and industry recently. Yet, till now there is little understanding about its performance in practice. In this paper, we make a benchmark measurement study of NFV software dataplanes in terms of packet processing capability, one of the most fundamental(More)
In this paper we learn the skills required by real-time physics-based avatars to perform parkour-style fast terrain crossing using a mix of running, jumping, speed-vaulting, and drop-rolling. We begin with a single motion capture example of each skill and then learn reduced-order linear feedback control laws that provide robust execution of the motions(More)
In this paper we present a physics-based framework for simulation and control of human-like skeleton-driven soft body characters. We couple the skeleton dynamics and the soft body dynamics to enable two-way interactions between the skeleton, the skin geometry, and the environment. We propose a novel pose-based plasticity model that extends the corotated(More)
The difficulty of developing control strategies has been a primary bottleneck in the adoption of physics-based simulations of human motion. We present a method for learning robust feedback strategies around given motion capture clips as well as the transition paths between clips. The output is a control graph that supports real-time physics-based simulation(More)
We address several limitations of the sampling-based motion control method of Liu et at. [LYvdP∗10]. The key insight is to learn from the past control reconstruction trials through sample distribution adaptation. Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently(More)
We introduce a method for learning low-dimensional linear feedback strategies for the control of physics-based animated characters around a given reference trajectory. This allows for learned low-dimensional state abstractions and action abstractions, thereby reducing the need to rely on manually designed abstractions such as the center-of-mass state or(More)
We present a chaotic hybrid invasive weed optimization (CHIWO) algorithm with an adaptive penalty function which is introduced to solve the constraints for solving the machinery optimizing problems. The proposed CHIWO algorithm runs consistently well on the studies of 13 Benchmark functions and 3 machinery optimizing problems. Experimental results indicate(More)