Browsing by Author "Zordan, Victor B."

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    Global Position Prediction for Interactive Motion Capture
    (ACM, 2021) Schreiner, Paul; Perepichka, Maksym; Lewis, Hayden; Darkner, Sune; Kry, Paul G.; Erleben, Kenny; Zordan, Victor B.; Narain, Rahul and Neff, Michael and Zordan, Victor
    We present a method for reconstructing the global position of motion capture where position sensing is poor or unavailable. Capture systems, such as IMU suits, can provide excellent pose and orientation data of a capture subject, but otherwise need post processing to estimate global position. We propose a solution that trains a neural network to predict, in real-time, the height and body displacement given a short window of pose and orientation data. Our training dataset contains pre-recorded data with global positions from many different capture subjects, performing a wide variety of activities in order to broadly train a network to estimate on like and unseen activities. We compare training on two network architectures, a universal network (u-net) and a traditional convolutional neural network (CNN) - observing better error properties for the u-net in our results. We also evaluate our method for different classes of motion. We observe high quality results for motion examples with good representation in specialized datasets, while general performance appears better in a more broadly sampled dataset when input motions are far from training examples.
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    Too Stiff, Too Strong, Too Smart: Evaluating Fundamental Problems with Motion Control Policies
    (ACM Association for Computing Machinery, 2023) Xie, Kaixiang; Xu, Pei; Andrews, Sheldon; Zordan, Victor B.; Kry, Paul G.; Wang, Huamin; Ye, Yuting; Victor Zordan
    Deep reinforcement learning (DRL) methods have demonstrated impressive results for skilled motion synthesis of physically based characters, and while these methods perform well in terms of tracking reference motions or achieving complex tasks, several concerns arise when evaluating the naturalness of the motion. In this paper, we conduct a preliminary study of specific quantitative metrics for measuring the naturalness of motion produced by DRL control policies beyond their visual appearance. Namely, we propose to study the stiffness of the control policy, in anticipation that it will influence how the character behaves in the presence of external perturbation. Second, we establish two baselines for strength that allow evaluating the use of joint torques in comparison to human performance. Third, we propose the study of variability to reveal the unnatural precision of control policies and how they compare to real human motion. In sum, we aim to establish repeatable measures to assess the naturalness of control policies produced by DRL methods, and we present a set of comparisons from state-of-the-art systems. Finally, we propose simple modifications to improve realism on these axes.