Staying Well Grounded in Markerless Motion Capture

Rosenhahn, Bodo; Schmaltz, Christian; Brox, Thomas; Weickert, Joachim; Seidel, Hans‐Peter

doi:10.1007/978-3-540-69321-5_39

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…In motion priors modeling, the prior knowledge from mocap database is sometimes embedded into implementation of some constraints. Rosenhahn et al employ geometric prior information about the movement pattern in markerless pose tracking process (Rosenhahn et al, 2008). Most of the work regarding reconstruction from video sequences has been done on human motion like (Yasin et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Retrieval, Recognition and Reconstruction of Quadruped Motions

Krüger

Yasin

Zsoldos

et al. 2014

Proceedings of the 9th International Conference on Computer Graphics Theory and Applications

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Abstract:Different techniques have been developed for capturing and retrieval, action recognition and video based reconstruction of human motion data in the past years. In this paper, we focus on how these techniques can be adapted to handle quadruped motion capture data and which new applications may appear. We discuss some particularities that must be considered during large animal motion capture. For retrieval, we derive suitable feature sets from quadrupeds motion capture data to perform fast searches for similar motions. Based on the retrieval techniques, the action recognition can be performed on the input motion capture sequences as well as on input video streams. We further present a data-driven approach to reconstruct quadruped motions from video data.

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Section: Related Workmentioning

confidence: 99%

Retrieval, Recognition and Reconstruction of Quadruped Motions

Krüger

Yasin

Zsoldos

et al. 2014

Proceedings of the 9th International Conference on Computer Graphics Theory and Applications

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“…They optimize their reconstruction framework with two steps gradient based optimization process without use of some data-driven prior knowledge. Rosenhahn et al [13] employ data-driven geometric ground plane prior constraints for human movement patterns in the process of pose tracking. Vondrak et al [10] perform Bayesian filtering based human motion tracking by full body physics based dynamic simulation priors together with interpolation of joint data.…”

Section: Related Workmentioning

confidence: 99%

Motion Tracking, Retrieval and 3D Reconstruction from Video

Yasin

Krüger

Weber

2014

IJMUE

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The work at hand presents a novel data-driven framework for 3D full body human motion reconstruction from uncalibrated monocular video data. To this end, we develop a knowledge base by taking 2D samples of the motion capture library from different viewing directions. This allows later steps to handle 2D query videos without any information on the viewing direction. We detect and track features from input video sequences by utilizing low-level image based feature detection techniques like MSER and SURF. This process is stabilized by back projection of high-level 3D prior information obtained from the motion capture library to the image plane. Extraction of suitable feature sets from both, input control signals and motion capture data, enables us to retrieve the best relevant prior poses from the motion capture library by employing fast motion retrieval techniques. Finally, 3D motion sequences are reconstructed by non-linear energy minimization, that takes into account multiple prior terms. Furthermore, we propose a method to estimate camera parameters from input video itself and sampling of motion capture library.

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“…Recently, priors that directly constrain kinematics with geometric constraints imposed by the environment have been introduced [37], [38]. While shown to be effective, these prior models can only constrain the location of the body segments with respect to the environment.…”

Section: Related Workmentioning

confidence: 99%

“…While shown to be effective, these prior models can only constrain the location of the body segments with respect to the environment. For example, such models can encode a constraint that feet should not penetrate the ground plane [37] or that feet or hands must be in some fixed configuration (as dictated by the environment) with respect to one another [38]. Such geometric priors are not able, however, to allow dynamically plausible environmental interactions, e.g., encode that feet must be in contact with the ground in such a way as to support the resulting motion, etc.…”

Section: Related Workmentioning

confidence: 99%

Dynamical Simulation Priors for Human Motion Tracking

Vondrak

Sigal

Jenkins

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We propose a simulation-based dynamical motion prior for tracking human motion from video in presence of physical ground-person interactions. Most tracking approaches to date have focused on efficient inference algorithms and/or learning of prior kinematic motion models; however, few can explicitly account for physical plausibility of recovered motion. Here, we aim to recover physically plausible motion of a single articulated human subject. Towards this end, we propose a full-body 3D physical simulationbased prior that explicitly incorporates a model of human dynamics into the Bayesian filtering framework. We consider the motion of the subject to be generated by a feedback "control loop" in which Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of interaction forces, motor forces and gravity. Interaction forces prevent physically impossible hypotheses, enable more appropriate reactions to the environment (e.g., ground contacts) and are produced from detected human-environment collisions. Motor forces actuate the body, ensure that proposed pose transitions are physically feasible and are generated using a motion controller. For efficient inference in the resulting high-dimensional state space, we utilize an exemplar-based control strategy that reduces the effective search space of motor forces. As a result, we are able to recover physically-plausible motion of human subjects from monocular and multi-view video. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to Bayesian filtering methods with standard motion priors.

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Staying Well Grounded in Markerless Motion Capture

Cited by 8 publications

References 12 publications

Retrieval, Recognition and Reconstruction of Quadruped Motions

Retrieval, Recognition and Reconstruction of Quadruped Motions

Motion Tracking, Retrieval and 3D Reconstruction from Video

Dynamical Simulation Priors for Human Motion Tracking

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