scholarly journals A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huaijun Wang ◽  
Dandan Du ◽  
Junhuai Li ◽  
Wenchao Ji ◽  
Lei Yu
Keyword(s):  
Motion Capture ◽  
Human Movement ◽  
Motion Capture Data ◽  
Style Transfer ◽  
Kinematic Constraints ◽  
Film And Television ◽  
Adversarial Network ◽  
Transfer Method ◽  
Motion Style ◽  
The Cost

Motion capture technology plays an important role in the production field of film and television, animation, etc. In order to reduce the cost of data acquisition and improve the reuse rate of motion capture data and the effect of movement style migration, the synthesis technology of motion capture data in human movement has become a research hotspot in this field. In this paper, kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Firstly, cycle-consistent adversarial network (CCycleGAN) is constructed, and the motion style migration network based on convolutional self-encoder is used as a generator to establish the cyclic consistent constraint between the generated motion and the content motion, so as to improve the action consistency between the generated motion and the content motion and eliminate the lag phenomenon of the generated motion. Then, kinematic constraints are introduced to normalize the movement generation, so as to solve the problems such as jitter and sliding step in the movement style migration results. Experimental results show that the generated motion of the cyclic consistent style transfer method with kinematic constraints is more similar to the style of style motion, which improves the effect of motion style transfer.

scholarly journals Descripción del movimiento humano basado en el marco de Frenet Serret y datos tipo MOCAP

2021 ◽  
Vol 17 (34) ◽  
pp. 170-180
Author(s):  
Juan Camilo Hernandez-Gomez ◽  
Alejandro Restrepo-Martínez ◽  
Juliana Valencia-Aguirre
Keyword(s):  
Motion Capture ◽  
Nearest Neighbors ◽  
Human Movement ◽  
The Body ◽  
Microsoft Kinect ◽  
Support Vector ◽  
Motion Capture Data ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
Optical Markers

Clasificar el movimiento humano se ha convertido en una necesidad tecnológica, en donde para definir la posición de un sujeto requiere identificar el recorrido de las extremidades y el tronco del cuerpo, y tener la capacidad de diferenciar esta posición respecto a otros sujetos o movimientos, generándose la necesidad tener datos y algoritmos que faciliten su clasificación. Es así, como en este trabajo, se evalúa la capacidad discriminante de datos de captura de movimiento en rehabilitación física, donde la posición de los sujetos es adquirida con el Kinect de Microsoft y marcadores ópticos, y atributos del movimiento generados con el marco de Frenet Serret, evaluando su capacidad discriminante con los algoritmos máquinas de soporte vectorial, redes neuronales y k vecinos más cercanos. Los resultados presentan porcentajes de acierto del 93.5% en la clasificación con datos obtenidos del Kinect, y un éxito del 100% para los movimientos con marcadores ópticos. Classify human movement has become a technological necessity, where defining the position of a subject requires identifying the trajectory of the limbs and trunk of the body, having the ability to differentiate this position from other subjects or movements, which generates the need to have data and algorithms that help their classification. Therefore, the discriminant capacity of motion capture data in physical rehabilitation is evaluated, where the position of the subjects is acquired with the Microsoft Kinect and optical markers. Attributes of the movement generated with the Frenet Serret framework. Evaluating their discriminant capacity by means of support vector machines, neural networks, and k nearest neighbors algorithms. The obtained results present an accuracy of 93.5% in the classification with data obtained from the Kinect, and success of 100% for movements where the position is defined with optical markers.

scholarly journals Improving the inverse kinematics reconstruction of human movement from motion capture data

PAMM ◽  
2010 ◽  
Vol 10 (1) ◽  
pp. 93-94
Author(s):  
Christian Simonidis ◽  
Wolfgang Seemann
Keyword(s):  
Motion Capture ◽  
Inverse Kinematics ◽  
Human Movement ◽  
Motion Capture Data

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

Active and Trust Based Packet Transfer Method via Route Tracking for Wireless Sensor Networks

2019 ◽  
Vol 8 (6S2) ◽  
pp. 221-224
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Sensor Systems ◽  
Remote Sensor ◽  
Dynamic Source ◽  
Transfer Method ◽  
The Cost ◽  
Ebb And Flow ◽  
The Way

Trust is critical in remote sensor systems to exchange the information from source to goal. The Dynamic Source Protocol computes the substitute way, if any hub neglects to exchange the information. The Dynamic Source Protocol does not have any worked in usefulness to figure a substitute way if the way has a vindictive hub. With the cost of an interloper recognition framework we can identify the vindictive hub and modify the information/parcel exchange way. Notwithstanding, gatecrasher location framework is extremely costly for remote sensor systems and there is no certification in identifying a malevolent hub. In the ebb and flow look into a trust-based approach is prescribed to limit the overheads of gatecrasher location framework and it likewise recognizes the anomalous conduct hubs. The proposed demonstrate utilizes the rehashed recreations to distinguish flawed hubs through the agreeable exertion in the sensor organize and additionally judges the trust of progressive hubs. Reenactments were exhibited for standardized result of parcel dropping, normal rebate result, and trust connection.

Perceptually motivated LSPIHT for motion capture data compression

2015 ◽  
Vol 51 ◽  
pp. 1-7 ◽  
Author(s):  
Irene Cheng ◽  
Amirhossein Firouzmanesh ◽  
Anup Basu
Keyword(s):  
Data Compression ◽  
Motion Capture ◽  
Motion Capture Data

scholarly journals Modeling and evaluating beat gestures for social robots

2021 ◽  
Author(s):  
Unai Zabala ◽  
Igor Rodriguez ◽  
José María Martínez-Otzeta ◽  
Elena Lazkano
Keyword(s):  
Motion Capture ◽  
Motion Capture System ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Desirable Feature ◽  
Quantitative Results ◽  
Path Lengths ◽  
End Effectors ◽  
The One ◽  
Made In

AbstractNatural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation.

Sign in / Sign up

Export Citation Format

Share Document