The key motivation regarding the development of distortion classifiers is that, for each different distortion class, we can select an appropriate method of suppressing (e.g., for the rectangular distortion, which is a result of mistaken marker labeling-it would be enough to find its counterpart marker and swap erroneous parts of the trajectories to achieve perfect reconstruction).
To our knowledge, our proposal is novel as it identifies erroneous intervals and classifies them accordingly. In related works, researchers have mainly focused on error prevention during gap reconstruction, focusing on the efficiency of error removal (including artifacts) in terms of root mean squared error ( R M S E).
#Motion artifact software#
It is a painstaking process that could be assisted with software support for trajectory reconstruction however, these capabilities are limited, and these methods could also degrade the results if used improperly.ĭespite the common knowledge about artifact problems, this topic has not been fully recognized.
#Motion artifact manual#
Since trajectory ’mis’-shapes are poorly filtered out by simple noise removing algorithms, standard industrial quality processing of mocap sequences require visual trajectory screenings and manual trajectory editing by operators. All distortions, gaps, and artifacts occur commonly in mocap sequences, influencing the praxis of a mocap operator. At higher levels of mocap processing, when the marker motion is remapped to drive the skeleton animation, the false positions of markers result in erroneous poses, which degrade the animation or biomechanical measurements. These distortions introduce trajectory modifications of different appearances and of larger amplitudes.
Numerous methods have been proposed utilizing low-pass filtering, interpolating methods, or moving averages.Īrtifact distortions are introduced by reconstruction algorithms present in mocap pipelines they could be regarded as momentary systematic errors. Among the types of noise, the most prominent is white noise, which can be efficiently filtered out or ‘smoothed’. Random noise is a consequence of the stochastic processes resulting in different kinds of distortions in a mocap sequence. Distortions occurring in the mocap sequences can be simply divided into two classes-random noise and algorithmically-introduced artifact distortions. The process of acquiring marker locations is error prone. Trajectories of these markers are then used for animation of associated skeletons, which are used as key models in the animation of human-like or animal characters. It utilizes visual tracking of active or retro-reflective passive markers. To date, the most reliable technology is the marker-based optical mocap (OMC)-it is known as the ‘gold standard’ as it outperforms other mocap technologies.
.png "motion artifact")
Prominent applications of mocap systems could also be found in biomechanics and medical sciences.
#Motion artifact movie#
Motion capture (mocap) systems play important roles in modern computer graphics, where they are applied in gaming and movie FX to generate realistic character animations.
0 kommentar(er)
