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Promptable Animal Pose Tracking Across Species — arXiv:2608.04995

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04995
License (as archived): CC0-1.0
Archived: 2026-10-31T11:48:16.636Z
SHA-256 of the archived text: 68a6b2fe497bb3a8bc72b0e9cf210a29b64852ea8c43bd4fd2b00561ef597132
Stable URL of this page: https://metraipolis.com/documento/dc_64fc2fb055a3

How to cite

Promptable Animal Pose Tracking Across Species — arXiv:2608.04995 — arXiv (en), https://arxiv.org/abs/2608.04995 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_64fc2fb055a3 (Metraipolis archive dc_64fc2fb055a3, sha256 68a6b2fe497bb3a8bc72b0e9cf210a29b64852ea8c43bd4fd2b00561ef597132)

BibTeX

@misc{dc64fc2fb055a3,
  title = {Promptable Animal Pose Tracking Across Species — arXiv:2608.04995},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04995},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_64fc2fb055a3; sha256 68a6b2fe497bb3a8bc72b0e9cf210a29b64852ea8c43bd4fd2b00561ef597132},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04995. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative. While human pose estimation and tracking has seen rapid progress thanks to large annotated datasets, animal pose remain challenging, due to large morphological and behavioural differences between species and limited annotated data. Existing approaches either optimise generic keypoint localisation from annotated datasets (such as APTv2) with poor generalisation, or track custom keypoints using visual tracking, at the cost of performance. In this paper, we demonstrate that vision foundation models trained on large datasets can be used effectively to track animal pose with limited labelled data. We propose two models, one unsupervised and the other supervised, to track user-selected keypoints in videos. The supervised approach delivers superior tracking accuracy by employing a keypoint prompt encoder to explicitly inject structural priors from a reference frame into feature matching. In parallel, the unsupervised route provides strong cross-species robustness by leveraging diverse foundation-model features for training-free correspondence matching. Extensive evaluation on challenging animal video benchmarks APTv2 and TigDog demonstrates that our framework achieves strong performance while maintaining an effective balance between accuracy and generalisation, offering a practical solution for real-world animal behaviour analysis and conservation applications.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-06 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.04995 — 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 2026-10-31T11:37:40.735Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).