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dc.contributor.authorFangbemi, Abassin Sourouen_US
dc.contributor.authorLu, Yi Feien_US
dc.contributor.authorXu, Maoyuanen_US
dc.contributor.authorLuo, Xiaowuen_US
dc.contributor.authorRolland, Alexisen_US
dc.contributor.authorRaissi, Chedyen_US
dc.contributor.editorHolden, Danielen_US
dc.date.accessioned2020-10-04T14:46:23Z
dc.date.available2020-10-04T14:46:23Z
dc.date.issued2020
dc.identifier.isbn978-3-03868-129-8
dc.identifier.issn1727-5288
dc.identifier.urihttps://doi.org/10.2312/sca.20201217
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sca20201217
dc.description.abstractThis work introduces a novel strategy for generating synthetic training data for 2D and 3D pose estimation of animals using keyframe animations. With the objective to automate the process of creating animations for wildlife, we train several 2D and 3D pose estimation models with synthetic data, and put in place an end-to-end pipeline called ZooBuilder. The pipeline takes as input a video of an animal in the wild, and generates the corresponding 2D and 3D coordinates for each joint of the animal's skeleton. With this approach, we produce motion capture data that can be used to create animations for wildlife.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectAnimation
dc.titleZooBuilder: 2D and 3D Pose Estimation for Quadrupeds Using Synthetic Dataen_US
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Showcases
dc.description.sectionheadersShowcases
dc.identifier.doi10.2312/sca.20201217
dc.identifier.pages1-2


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