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Hidden Markov model is used in the motion sequence to generate a motion transition probability matrix.
The system first detects 2-D "keypoints" on the subject's body, and turns these poses into a series of 3-D "skeletons" that are stitched together to generate a motion sculpture.
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Interpolation of reference samples to generate a motion-compensated prediction is generally performed for each macroblock that is intercoded [12] and occupies most complexity in motion-compensated prediction.
After stitching these skeletons together, the system generates a motion sculpture that can be 3-D-printed, showing the smooth, continuous path of movement traced out by the subject.
We compare HC and RC using a grid world and a physical robot singulation task, where in the latter the input is a binary image of a connected set of objects on a planar worksurface and the policy generates a motion of the gripper to separate one object from the rest.
MoSculpt connects these skeletons and generates a motion sculpture that the user can 3D print.
Mapping of successive instant centres throughout the range of motion, however, does not generate a single motion axis.
Indeed, due to the FPSE behavior, it is vital to achieve a stable periodic motion, and the only strategy is to generate a periodic motion with a set of parameters in the displacer piston [4, 7, 16].
This bias magnetic field enables the motor to generate a swing motion.
There exists a kinematic connection between two surfaces linked through a curved crease that can be used to generate a folding motion.
The searching method is based on an evolutionary algorithm and it is able to generate a local motion plan using continuously updated sensor information.
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