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Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising

Zhou, Kanglei; Shum, Hubert P.H.; Li, Frederick W.B.; Liang, Xiaohui

Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising Thumbnail


Authors

Kanglei Zhou

Xiaohui Liang



Abstract

In many human-computer interaction applications, fast and accurate hand tracking is necessary for an immersive experience. However, raw hand motion data can be flawed due to issues such as joint occlusions and high-frequency noise, hindering the interaction. Using only current motion for interaction can lead to lag, so predicting future movement is crucial for a faster response. Our solution is the Multi-task Spatial-Temporal Graph Auto-Encoder (Multi-STGAE), a model that accurately denoises and predicts hand motion by exploiting the inter-dependency of both tasks. The model ensures a stable and accurate prediction through denoising while maintaining motion dynamics to avoid over-smoothed motion and alleviate time delays through prediction. A gate mechanism is integrated to prevent negative transfer between tasks and further boost multi-task performance. Multi-STGAE also includes a spatial-temporal graph autoencoder block, which models hand structures and motion coherence through graph convolutional networks, reducing noise while preserving hand physiology. Additionally, we design a novel hand partition strategy and hand bone loss to improve natural hand motion generation. We validate the effectiveness of our proposed method by contributing two large-scale datasets with a data corruption algorithm based on two benchmark datasets. To evaluate the natural characteristics of the denoised and predicted hand motion, we propose two structural metrics. Experimental results show that our method outperforms the state-of-the-art, showcasing how the multitask framework enables mutual benefits between denoising and prediction.

Citation

Zhou, K., Shum, H. P., Li, F. W., & Liang, X. (2023). Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. IEEE Transactions on Visualization and Computer Graphics, https://doi.org/10.1109/TVCG.2023.3337868

Journal Article Type Article
Acceptance Date Nov 27, 2023
Online Publication Date Nov 30, 2023
Publication Date Nov 30, 2023
Deposit Date Nov 29, 2023
Publicly Available Date Nov 30, 2023
Journal IEEE Transactions on Visualization and Computer Graphics
Print ISSN 1077-2626
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TVCG.2023.3337868
Public URL https://durham-repository.worktribe.com/output/1962816

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