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Article overview
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Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation | | Leyla Khaleghi
; Joshua Marshall
; Ali Etemad
; | | Date: |
1 Jun 2022 | | Abstract: | 3D hand pose estimation (HPE) is the process of locating the joints of the
hand in 3D from any visual input. HPE has recently received an increased amount
of attention due to its key role in a variety of human-computer interaction
applications. Recent HPE methods have demonstrated the advantages of employing
videos or multi-view images, allowing for more robust HPE systems. Accordingly,
in this study, we propose a new method to perform Sequential learning with
Transformer for Hand Pose (SeTHPose) estimation. Our SeTHPose pipeline begins
by extracting visual embeddings from individual hand images. We then use a
transformer encoder to learn the sequential context along time or viewing
angles and generate accurate 2D hand joint locations. Then, a graph
convolutional neural network with a U-Net configuration is used to convert the
2D hand joint locations to 3D poses. Our experiments show that SeTHPose
performs well on both hand sequence varieties, temporal and angular. Also,
SeTHPose outperforms other methods in the field to achieve new state-of-the-art
results on two public available sequential datasets, STB and MuViHand. | | Source: | arXiv, 2206.00171 | | Services: | Forum | Review | PDF | Favorites | |
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