Metraipolis › archive › dc_366af6f96529

CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image — arXiv:2609.00995

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00995
License (as archived): CC0-1.0
Archived: 2026-10-17T07:13:45.297Z
SHA-256 of the archived text: e1e5285bd0cbd54333e600520a8c50fd0ef4f33a720caf4278cf0a421c092b1c
Stable URL of this page: https://metraipolis.com/documento/dc_366af6f96529

How to cite

CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image — arXiv:2609.00995 — arXiv (en), https://arxiv.org/abs/2609.00995 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_366af6f96529 (Metraipolis archive dc_366af6f96529, sha256 e1e5285bd0cbd54333e600520a8c50fd0ef4f33a720caf4278cf0a421c092b1c)

BibTeX

@misc{dc366af6f96529,
  title = {CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image — arXiv:2609.00995},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00995},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_366af6f96529; sha256 e1e5285bd0cbd54333e600520a8c50fd0ef4f33a720caf4278cf0a421c092b1c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00995. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recovering 3D digital humans from a single 2D image is an ill-posed computer vision problem due to the loss of depth information. Probabilistic 3D human pose estimation compensates for this by estimating a set of 3D hypotheses from a prior distribution via generative models. However, most prior work focuses only on 3D keypoints, which often leads to implausible poses that are difficult to apply to downstream tasks, e.g. animation or digital humans. SMPL-based methods are more scalable thanks to the explicit body priors, but it requires more complex modeling of the generation process due to the non-additive nature of the joint rotations. In this work, we propose a novel approach for probabilistic 3D humans using quaternion-constrained continuous normalizing flows conditioned on 2D pose estimations. Our proposed quaternion flows show significant advantages over approaches using other rotation representations. Experiments demonstrate state-of-the-art results of our method on Human3.6M, particularly in ambiguous settings, and comparable pose estimation accuracy on challenging 3DPW and EMDB benchmarks.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-02 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.00995 — 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 2026-10-17T07:00:58.957Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).