Modeling of Human Body for Animation by Micro-sensor Motion Capture

Author(s):  
Gang Li ◽  
Zheng Wu ◽  
Xiaoli Meng ◽  
Jiankang Wu
Author(s):  
Zhang Nan ◽  
Shunyan Sun ◽  
Jiankang Wu ◽  
Xiaoli Meng ◽  
Guanhong Tao

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mingming Zhang ◽  
Lu Yu ◽  
Keye Zhang ◽  
Bixuan Du ◽  
Bin Zhan ◽  
...  

Abstract Human body movements can convey a variety of emotions and even create advantages in some special life situations. However, how emotion is encoded in body movements has remained unclear. One reason is that there is a lack of public human body kinematic dataset regarding the expressing of various emotions. Therefore, we aimed to produce a comprehensive dataset to assist in recognizing cues from all parts of the body that indicate six basic emotions (happiness, sadness, anger, fear, disgust, surprise) and neutral expression. The present dataset was created using a portable wireless motion capture system. Twenty-two semi-professional actors (half male) completed performances according to the standardized guidance and preferred daily events. A total of 1402 recordings at 125 Hz were collected, consisting of the position and rotation data of 72 anatomical nodes. To our knowledge, this is now the largest emotional kinematic dataset of the human body. We hope this dataset will contribute to multiple fields of research and practice, including social neuroscience, psychiatry, computer vision, and biometric and information forensics.


Author(s):  
Chengkai Wan ◽  
Baozong Yuan ◽  
Yunda Sun ◽  
Zhenjiang Miao

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 601-610 ◽  
Author(s):  
Jihong Lee ◽  
Insoo Ha

In this paper we propose a set of techniques for a real-time motion capture of a human body. The proposed motion capture system is based on low cost accelerometers, and is capable of identifying the body configuration by extracting gravity-related terms from the sensor data. One sensor unit is composed of 3 accelerometers arranged orthogonally to each other, and is capable of identifying 2 rotating angles of joints with 2 degrees of freedom. A geometric fusion technique is applied to cope with the uncertainty of sensor data. A practical calibration technique is also proposed to handle errors in aligning the sensing axis to the coordination axis. In the case where motion acceleration is not negligible compared with gravity acceleration, a compensation technique to extract gravity acceleration from the sensor data is proposed. Experimental results not only for individual techniques but also for human motion capturing with graphics are included.


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