mlsanityData to 7 Oct 2026

HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning

arXiv 2302.00988 ↗First posted 2 Feb 2023cs.CVcited by 7

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Abstract (arXiv)

Recent advancements in 3D hand pose estimation have shown promising results, but its effectiveness has primarily relied on the availability of large-scale annotated datasets, the creation of which is a laborious and costly process. To alleviate the label-hungry limitation, we propose a self-supervised learning framework, HaMuCo, that learns a single-view hand pose estimator from multi-view pseudo 2D labels. However, one of the main challenges of self-supervised learning is the presence of noisy labels and the ``groupthink'' effect from multiple views. To overcome these issues, we introduce a cross-view interaction network that distills the single-view estimator by utilizing the cross-view correlated features and enforcing multi-view consistency to achieve collaborative learning. Both the single-view estimator and the cross-view interaction network are trained jointly in an end-to-end manner. Extensive experiments show that our method can achieve state-of-the-art performance on multi-view self-supervised hand pose estimation. Furthermore, the proposed cross-view interaction network can also be applied to hand pose estimation from multi-view input and outperforms previous methods under the same settings.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 52 of its 69 references that are in the map. First posted on arXiv 2 Feb 2023.

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52 of 69 references.

References · 52

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38 more
  1. 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 4.

7 papers.