Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data

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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks. It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments. Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion manifold. The segmentation task however often remains a manual task, which increases the effort and cost of generating large-scale motion databases. We therefore propose an automatic framework for semantic segmentation of motion capture data using a dilated temporal fully-convolutional network. Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling. We further show our model is robust against high noisy training labels.
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@inproceedings{
10.2312:sca.20181185
, booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
}, editor = {
Skouras, Melina
}, title = {{
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data
}}, author = {
Noshaba, Cheema
and
Hosseini, Somayeh
and
Sprenger, Janis
and
Herrmann, Erik
and
Du, Han
and
Fischer, Klaus
and
Slusallek, Philipp
}, year = {
2018
}, publisher = {
The Eurographics Association
}, ISSN = {
1727-5288
}, ISBN = {
978-3-03868-070-3
}, DOI = {
10.2312/sca.20181185
} }
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