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dc.contributor.authorWang, Yangangen_US
dc.contributor.authorRao, Rutingen_US
dc.contributor.authorZou, Changqingen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.description.abstractPersonalized hand models can be utilized to synthesize high quality hand datasets, provide more possible training data for deep learning and improve the accuracy of hand pose estimation. In recent years, parameterized hand models, e.g., MANO, are widely used for obtaining personalized hand models. However, due to the low resolution of existing parameterized hand models, it is still hard to obtain high-fidelity personalized hand models. In this paper, we propose a new method to estimate personalized hand models from multiple hand postures with multi-view color images. The personalized hand model is represented by a personalized neutral hand, and multiple hand postures. We propose a novel optimization strategy to estimate the neutral hand from multiple hand postures. To demonstrate the performance of our method, we have built a multi-view system and captured more than 35 people, and each of them has 30 hand postures.We hope the estimated hand models can boost the research of highfidelity parameterized hand modeling in the future. All the hand models are publicly available on
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.titlePersonalized Hand Modeling from Multiple Postures with Multi-View Color Imagesen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersHuman Pose

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  • 39-Issue 7
    Pacific Graphics 2020 - Symposium Proceedings

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