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SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation — arXiv:2609.08108

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08108
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:05.290Z
SHA-256 of the archived text: 4970ecf6a48dde094e2222b9036cbb50133331978f02b1a73e9b15cb37418cfa
Stable URL of this page: https://metraipolis.com/documento/dc_463e5e2cdca8

How to cite

SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation — arXiv:2609.08108 — arXiv (en), https://arxiv.org/abs/2609.08108 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_463e5e2cdca8 (Metraipolis archive dc_463e5e2cdca8, sha256 4970ecf6a48dde094e2222b9036cbb50133331978f02b1a73e9b15cb37418cfa)

BibTeX

@misc{dc463e5e2cdca8,
  title = {SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation — arXiv:2609.08108},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08108},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_463e5e2cdca8; sha256 4970ecf6a48dde094e2222b9036cbb50133331978f02b1a73e9b15cb37418cfa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08108. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited by the small scale, restricted viewpoints, and limited visual diversity of existing datasets. We introduce SynthGait-19k, a physically grounded synthetic video dataset containing 19,272 walking videos derived from 6,427 MoCap sequences across 437 subjects, with paired SMPL motion and annotations for six gait parameters. To construct the dataset, we develop Gait2Vid, which unifies heterogeneous MoCap recordings through SMPL and synthesizes diverse RGB walking videos under controllable viewpoints and scene appearances. We assess the generated videos for consistency with their conditioning gait kinematics and validate extracted gait events against force-platform measurements. Using SynthGait-19K, we benchmark direct RGB, pose-based, biomechanical, and human-mesh-recovery approaches and analyze viewpoint, training-data scale, and synthetic-to-real domain shift. We also introduce GaitXFormer as a direct RGB reference model for estimating gait parameters. Synthetic supervision transfers effectively to real videos across both GaitXFormer and a pose-based architecture, demonstrating utility across different representations. We further find that spatial gait parameters are more sensitive to visual domain shift and that improved HMR reconstruction alone does not necessarily translate to improved downstream gait estimation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.08108 — 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 2026-10-16T14:49:57.275Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).