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doi:10.22028/D291-39326
Titel: | Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks |
VerfasserIn: | Cheema, Noshaba Hosseini, Somayeh Sprenger, Janis Herrmann, Erik Du, Han Fischer, Klaus Slusallek, Philipp |
Sprache: | Englisch |
Verlag/Plattform: | arXiv |
Erscheinungsjahr: | 2019 |
DDC-Sachgruppe: | 004 Informatik 400 Sprache, Linguistik |
Dokumenttyp: | Sonstiges |
Abstract: | Human motion capture data has been widely used in data-driven character animation. In order to generate realistic, natural-looking motions, most data-driven approaches require considerable efforts of pre-processing, including motion segmentation and annotation. Existing (semi-) automatic solutions either require hand-crafted features for motion segmentation or do not produce the semantic annotations required for motion synthesis and building large-scale motion databases. In addition, human labeled annotation data suffers from inter- and intra-labeler inconsistencies by design. We propose a semi-automatic framework for semantic segmentation of motion capture data based on supervised machine learning techniques. It first transforms a motion capture sequence into a ``motion image'' and applies a convolutional neural network for image segmentation. Dilated temporal convolutions enable the extraction of temporal information from a large receptive field. Our model outperforms two state-of-the-art models for action segmentation, as well as a popular network for sequence modeling. Most of all, our method is very robust under noisy and inaccurate training labels and thus can handle human errors during the labeling process. |
DOI der Erstveröffentlichung: | 10.48550/arXiv.1903.00695 |
URL der Erstveröffentlichung: | https://arxiv.org/abs/1903.00695 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-393265 hdl:20.500.11880/35453 http://dx.doi.org/10.22028/D291-39326 |
Datum des Eintrags: | 17-Mär-2023 |
Bemerkung/Hinweis: | Preprint |
Fakultät: | MI - Fakultät für Mathematik und Informatik |
Fachrichtung: | MI - Informatik |
Professur: | MI - Prof. Dr. Philipp Slusallek |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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