Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image da...

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Bibliographic Details
Published inProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 5345 - 5354
Main Authors Zhou, Yuxiao, Habermann, Marc, Xu, Weipeng, Habibie, Ikhsanul, Theobalt, Christian, Xu, Feng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
Subjects
Detectors
Feature extraction
Heating systems
Kinematics
Predictive models
Three-dimensional displays
Two dimensional displays
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ISSN1063-6919
DOI10.1109/CVPR42600.2020.00539

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Summary:We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image data with either 2D or 3D annotations, as well as stand-alone 3D animations without corresponding image data. It features a 3D hand joint detection module and an inverse kinematics module which regresses not only 3D joint positions but also maps them to joint rotations in a single feed-forward pass. This output makes the method more directly usable for applications in computer vision and graphics compared to only regressing 3D joint positions. We demonstrate that our architectural design leads to a significant quantitative and qualitative improvement over the state of the art on several challenging benchmarks. We will make our code publicly available for future research.
ISSN:1063-6919
DOI:10.1109/CVPR42600.2020.00539