Diverse human motion prediction based on orthogonal basis vectors

Hanqing Tong; Wenwen Ding; Qing Li; Xin Wang

doi:10.1117/12.3055768

9 January 2025 Diverse human motion prediction based on orthogonal basis vectors

Hanqing Tong, Wenwen Ding, Qing Li, Xin Wang

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134862N (2025) https://doi.org/10.1117/12.3055768
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

The diversity human motion prediction task predicts multiple future motion sequences from historical data. Existing research uses likelihood-based sampling, but human motion's inhomogeneity often causes modal collapse and complex training. Our paper proposes a method using a linear dynamical system to model spatiotemporal dependence, obtaining orthogonal basis vectors via Tucker decomposition. By connecting these with encoded motion residuals and sampling the Grassmann manifold with a relaxed Bernoulli distribution, we predict future motions. Compared to existing methods on Human3.6M and HumanEva-I datasets, our approach mitigates pattern collapse, improves diversity by 3%, and reduces average error by 0.07.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanqing Tong, Wenwen Ding, Qing Li, and Xin Wang "Diverse human motion prediction based on orthogonal basis vectors", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134862N (9 January 2025); https://doi.org/10.1117/12.3055768

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