• Journal of IKEEE
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• v.23 no.4
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• pp.1295-1301
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• 2019

In this paper, we propose a novel method of denoising human motion using a bidirectional recurrent neural network (BRNN) with an attention mechanism. The corrupted motion captured from a single 3D depth sensor camera is automatically fixed in the well-established smooth motion manifold. Incorporating an attention mechanism into BRNN achieves better optimization results and higher accuracy than other deep learning frameworks because a higher weight value is selectively given to a more important input pose at a specific frame for encoding the input motion. Experimental results show that our approach effectively handles various types of motion and noise, and we believe that our method can sufficiently be used in motion capture applications as a post-processing step after capturing human motion.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1627-1633
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• 2017

Human dance is difficult to learn since there is no effective way to imitate an expert's motion, a sequence of complicated body movements, without taking an actual class. In this paper, we propose a dance learning system using human depth information. In the proposed system, a set of example motions are captured from various expert dancers through a marker-free motion capture and archived into a motion database server for online dance lessons. Given the end-user devices such as tablet and kiosk PCs, a student can learn a desired motion selected from the database and send one's own motion to an instructor for online feedback. During this learning process, our system provides a posture-based motion search and multi-mode views to support the efficient exchange of motion data between the student and instructor under a networked environment. The experimental results demonstrate that our system is capable to improve the student's dance skills over a given period of time.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.287-299
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• 2008

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.805-809
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• 2003

We developed a human-sized BWR(biped walking robot) named KUBIR1 driven by a new actuator based on the ball screw which has high strength and high gear ratio. KUBIR1 was developed to walk autonomously such that it is actuated by small torque motors and is boarded with DC battery and controllers. To utilize the information on the human walking motion and to analyze the walking mode of robot, a motion capture system was developed. The system is composed of the mechanical and electronic devices to obtain the joint angle data. By using the obtained data, a 3-D graphic interface was developed based on the OpenGL tool. Through the graphic interface, the control input of KUBIR1 is performed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.507-510
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• 2002

We developed a human-sized BWR(biped walking robot) named KUBIRI driven by a new actuator based on the ball screw which has high strength and high gear ratio. KUBIRI was developed to walk autonomously such that it is actuated by small torque motors and is boarded with DC battery and controllers. To utilize informations on the human walking motion and to analyze the walking mode of robot, a motion capture system was developed. The system is composed of the mechanical and electronic devices to obtain the joint angle data. By using the obtained data, a 3-D graphic interfacer was developed based on the open inventor tool. Through the graphic interfacer, the control input of KUBIRI is performed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.5-7
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• 2021

Motion capture data files for pose estimation may have inaccurate data depending on the surrounding environment and the degree of movement, so it is necessary to correct it. In the past, inaccurate data was restored with post-processing by people, but recently various kind of neural networks such as LSTM and R-CNN are used as automated method. However, since neural network-based data restoration methods require a lot of computing resource, this paper proposes a method that reduces computing resource and maintains data restoration rate compared to neural network-based method. The proposed method automatically restores inaccurate motion capture data by using posture measurement data (c3d). As a result of the experiment, data restoration rates ranged from 89% to 99% depending on the degree of inaccuracy of the data.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1517-1527
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• 2011

This paper presents a motion capture based rehabilitation training system for a lower-limb paretic patient. The system evaluates the rehabilitation status of the patient by using the bend posture of the knees and the weight balance of the body. The posture of both legs is captured with a single camera using the planar mirror. The weight distribution is obtained by the Wii Balance Board. Self-occlusion problem in the tracking of the legs is resolved by using k-nearest neighbor based clustering with body symmetry and local-linearity of the posture data. To do this, we present data normalization and its symmetric property in the normalized vector space.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.239-247
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• 2017

The purpose of this study is to develop a motion generation technique based on a double inverted pendulum model (DIPM) that learns and reproduces humanoid robot (or virtual human) motions while keeping its balance in a pattern similar to a human. DIPM consists of a cart and two inverted pendulums, connected in a serial. Although the structure resembles human upper- and lower-body, the balancing motion in DIPM is different from the motion that human does. To do this, we use the motion capture data to obtain the reference motion to keep the balance in the existence of external force. By an optimization technique minimizing the difference between the motion of DIPM and the reference motion, control parameters of the proposed method were learned in advance. The learned control parameters are re-used for the control signal of DIPM as input of linear quadratic regulator that generates a similar motion pattern as the reference. In order to verify this, we use virtual human experiments were conducted to generate the motion that naturally balanced.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.528-536
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• 2020

As humanoid robot technology advances, the use of robots for performance is increasing. As a result, studies are being conducted to increase the scope of use of robots in performances by making them natural like humans. Among them, the use of motion capture technology is often used, and there are environmental inconveniences in preparing for motion capture, such as the need for IMU sensors or markers attached to each part of the body and precise high-performance cameras. In addition, for robots used in performance technology, there is a problem that they have to respond in real time depending on the unexpected situation or the audience's response. To make up for the above mentioned problems, in this paper, we proposed a real-time motion capture system by using a number of RGB-D cameras and creating natural robot motion similar to human motion by using motion-captured data.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1092-1101
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• 2003

Most of 3D character animation adjusts the gait of their characters for various terrains, using motion capture data through the motion capture equipments. This motion capture data can be naturally presented as real human motions, which are to be adjusted according to the various types of terrain. In addition, there would be a difficulty applying motion capture data for other characters in which the motion data will be captured again or edited for the existing motion data. Therefore, this paper proposes a method that is to generate walking motion for various terrains, such as flat, inclined plane, stair, and irregular face, and a method that is to calculate the trajectory of the swing leg and pelvis. These methods are able to generate various gaits controlled by the parameters of body height, walking speed, stride, etc. In addition, the positions and angles of joint can be calculated by using inverse kinematics, and the cubic spline will be used to calculate the trajectory of the joint.