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.
Description
@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}
}