| Autor: |
Murthy, Pramod, Butt, Hammad, Hiremath, Sandesh, Khoshhal, Alireza, Stricker, Didier |
| Zdroj: |
IPSJ Transactions on Computer Vision and Applications; December 2019, Vol. 11 Issue: 1 p1-9, 9p |
| Abstrakt: |
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 (s1,s2,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. |
| Databáze: |
Supplemental Index |
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