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- W2955254493 abstract "Abstract Realistic estimation and synthesis of articulated human motion must satisfy anatomical constraints on joint angles. A data-driven approach is used to learn human joint limits from 3D motion capture datasets. We represent joint constraints with a new formulation ( s 1 , s 2 , τ ) using swing-twist representation in exponential maps form. Our parameterization is applied on Human3.6M dataset to create the lookup-map for each joint. These maps enable us to generate ‘synthetic’ datasets in entire joint rotation space of a given joint. A set of neural network discriminators is then trained with synthetic datasets to learn valid/invalid joint rotations. The discriminators achieve accuracy of [94.4−99.4 % ] for different joints. We validate precision-accuracy trade-off of discriminators and qualitatively evaluate classified poses with an interactive tool. The learned discriminators can be used as ‘priors’ for human pose estimation and motion synthesis." @default.
- W2955254493 created "2019-07-12" @default.
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- W2955254493 date "2019-06-25" @default.
- W2955254493 modified "2023-09-23" @default.
- W2955254493 title "Learning 3D joint constraints from vision-based motion capture datasets" @default.
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- W2955254493 doi "https://doi.org/10.1186/s41074-019-0057-z" @default.
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