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Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos — arXiv:2609.34149

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34149
License (as archived): CC0-1.0
Archived: 2026-10-07T23:51:54.432Z
SHA-256 of the archived text: 3fe31276a7b5ea5d83f534278f332a42de210143d020796cc9e5195fb17210e3
Stable URL of this page: https://metraipolis.com/documento/dc_ac00a6d7aaac

How to cite

Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos — arXiv:2609.34149 — arXiv (en), https://arxiv.org/abs/2609.34149 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ac00a6d7aaac (Metraipolis archive dc_ac00a6d7aaac, sha256 3fe31276a7b5ea5d83f534278f332a42de210143d020796cc9e5195fb17210e3)

BibTeX

@misc{dcac00a6d7aaac,
  title = {Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos — arXiv:2609.34149},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34149},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ac00a6d7aaac; sha256 3fe31276a7b5ea5d83f534278f332a42de210143d020796cc9e5195fb17210e3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34149. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Current methods for egocentric view action recognition often face challenges in perceiving dynamic hand movements relying solely on geometrical or physical information. In this work, we effectively address this problem by gaining insights into the correlation between functional hand configurations and objects, which improves the detailed interpretation of real-world scenarios. To this end, we introduce a practical taxonomy of hand types based on the functioning perspective and utilize it for per-frame hand type labeling on existing datasets. We also propose a novel hand action recognition framework considering semantic details of the hand type as prior. This approach boosts the network's understanding of the continuous hand interaction throughout the action sequence. Our whole pipeline consists of three main modules: (1) Feature Extraction, (2) Egocentric Knowledge Module, which estimates 3D hand pose, object category, and hand type leveraging short-term cues, and (2) Egocentric Action Module, which aggregates per-frame knowledge, including text embeddings of hand type, over a longer time. In our extensive experiments with large-scale benchmarks, FPHA and H2O, our model outperforms current state-of-the-art methods, demonstrating its superior performance.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.34149 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:29:27.300Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).