This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user's full-body locomotion and the IMU's data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character's full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.
The approach presented in this paper is similar to previous methods in terms of reconstructing the full-body locomotion of a virtual character. Compared with Slyper and Hodgins' @cite_17 method, the present one not only searches in a motion database for the closest motion but also interpolates between them to reconstruct a new motion that follows the user's input trajectory. Smoother results with fewer reconstruction errors were achieved compared with 's method @cite_41 . The closest solution to the approach presented here is the one proposed by @cite_47 . The major disadvantage of 's method is that it can only reconstruct a single locomotion behavior at a time and cannot reconstruct motions such as running on a curved path (see Figure ) or even continuous locomotion with multiple behaviors (see accompanying video). In contrast, the proposed method can reconstruct any locomotion type (e.g., walking, running, jumping, hopping, and side stepping) in any row with any variation (e.g., turning during walking or turning during running). Therefore, the main advantage of the presented statistical model is its ability to reconstruct long locomotion sequences with different behaviors in real-time by using input data provided by a single sensor.
@cite_41 @cite_47 @cite_17
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This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skelet al size from a small set of 2D image features tracked from uncalibrated monocular video sequences. The key idea of our approach is to construct a generative human motion model from a large set of preprocessed human motion examples to constrain the solution space of monocular human motion tracking. In addition, we learn a generative skeleton model from prerecorded human skeleton data to reduce ambiguity of the human skeleton reconstruction. We formulate the reconstruction process in a nonlinear optimization framework by continuously deforming the generative models to best match a small set of 2D image features tracked from a monocular video sequence. We evaluate the performance of our system by testing the algorithm on a variety of uncalibrated monocular video sequences. Generating a visually appealing human motion sequence using low-dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system. This paper describes experiments regarding navigation performance using a new locomotion interface for walking through virtual space. Although traveling on foot is the most intuitive style of locomotion, proprioceptive feedback from walking is not provided in most applications of virtual environments. We developed an infinite surface driven by actuators for enabling a sense of walking. Torus-shaped surfaces are selected to realize the locomotion interface. The device employs twelve sets of treadmills, connected side by side and driven in perpendicular directions. The virtual infinite surface is generated by the motion of the treadmills. A walker can go in any direction while his her position is fixed in the real world. The device is called a Torus Treadmill. Navigation performance was measured by path-reproduction tests. Subjects were immersed in a virtual grass-covered plain on which a cone-shaped target object was placed. The subjects first traveled to the target object. After they reached it, the target object disappeared and the rehomed subjects were asked to return to the place where the target object was placed. We also set two target objects, and the subject traveled along a bent path. We compared two locomotion modes: walking on the Torus Treadmill and moving purely by joystick operation. The results of the bent-path experiment showed that the accuracy of the path reproduction in the Torus Treadmill mode is better than that of joystick mode. We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the behavior set and the sharing pattern are both inferred from data. We develop Markov chain Monte Carlo (MCMC) methods based on the Indian buffet process representation of the predictive distribution of the beta process. Our MCMC inference algorithm efficiently adds and removes behaviors via novel split-merge moves as well as data-driven birth and death proposals, avoiding the need to consider a truncated model. We demonstrate promising results on unsupervised segmentation of human motion capture data. 1. Introduction. Classical time series analysis has generally focused on the study of a single (potentially multivariate) time series. Instead, we consider analyzing collections of related time series, motivated by the increasing abundance of such data in many domains. In this work we explore this problem by considering time series produced by motion capture sensors on the joints of people performing exercise routines. An individual recording provides a multivariate time series that can be segmented into types of exercises (e.g., jumping jacks, arm-circles, and twists). Each exercise type describes locally coherent and simple dynamics that persist over a segment of time. We have such motion capture recordings from multiple individuals, each of whom performs some subset of a global set of exercises, as shown in Figure1. Our goal is to discover the set of global exercise types (“behaviors”) and their occurrences in each individual’s data stream. We would like to take advantage of the overlap between individuals: if a jumping-jack behavior is discovered in one sequence, then it can be used to model data for other individuals.