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• W4366606483 abstract "Estimating three-dimensional (3D) human poses from two-dimensional (2D) joints has achieved promising results. However, there is relatively little work focused on exploiting domain-specific knowledge as prior. In this work, we present a learning framework based on prior knowledge for the task of estimating a 3D human pose from a 2D pose. In contrast to other state-of-the-art 3D pose estimation approaches, the proposed method is a systematic analysis pipeline that takes full advantage of prior knowledge based on three observations. The proposed approach can model the spatial and temporal relations between joints to achieve better performance. Our approach formulates the learning network as an encoder–decoder architecture that explicitly encodes prior knowledge about the task. The encoder is a multi-head self-attention network which can capture human joint spatial relations. The decoder is formulated as three separated sub-networks, each sub-network represents a kinematic chain which is derived from our prior knowledge about human motion. The experimental results on the Human3.6M, HumanEva and MPI-INF-3DHP datasets demonstrate the effectiveness of our approach. The code and data are available at https://github.com/XTU-PR-LAB/PK-SAN." @default.
• W4366606483 created "2023-04-23" @default.
• W4366606483 creator A5044614980 @default.
• W4366606483 creator A5062015169 @default.
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• W4366606483 date "2023-09-01" @default.
• W4366606483 modified "2023-10-05" @default.
• W4366606483 title "Prior-knowledge-based self-attention network for 3D human pose estimation" @default.
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• W4366606483 doi "https://doi.org/10.1016/j.eswa.2023.120213" @default.
• W4366606483 hasPublicationYear "2023" @default.
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