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WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI — arXiv:2610.07025

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07025
License (as archived): CC0-1.0
Archived: 2026-10-07T13:06:57.145Z
SHA-256 of the archived text: 59faf594289b2f9866f734e1d289f1bbe120fc5ce4b6cc33b30e45ed0ed42ce1
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How to cite

WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI — arXiv:2610.07025 — arXiv (en), https://arxiv.org/abs/2610.07025 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_166fc4a281dc (Metraipolis archive dc_166fc4a281dc, sha256 59faf594289b2f9866f734e1d289f1bbe120fc5ce4b6cc33b30e45ed0ed42ce1)

BibTeX

@misc{dc166fc4a281dc,
  title = {WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI — arXiv:2610.07025},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07025},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_166fc4a281dc; sha256 59faf594289b2f9866f734e1d289f1bbe120fc5ce4b6cc33b30e45ed0ed42ce1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07025. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-person 3D pose estimation with WiFi channel state information (CSI) is challenging because reflections from different people overlap without directly identifying individual joints. Existing masked embedding objectives capture wireless relationships without explicit pose supervision, while structured decoders can retain coordinate errors. We propose WiSPER, a two-stage framework combining pose-aware predictive pretraining with conditional residual flow refinement. Pose-Aware Masked Embedding Learning (PAMEL) couples masked latent prediction with auxiliary pose-set supervision on the same CSI context, guiding the encoder toward joint localization from partial observations. Residual Flow refinement with Transformer (ReFT) generates a set of pose candidates to accommodate a variable number of people and refines each candidate through a conditional flow guided by its coarse coordinates and per-joint decoder features. Both stages use paired CSI and pose annotations during training, while inference requires only CSI. Experiments on the PiW3D dataset show that WiSPER achieves an overall mean per-joint position error of 63.72 mm, a 40.0% reduction relative to WiFi-JEPA. For experiments with two and three people, WiSPER reduces MPJPE by 42.1% and 38.1%, respectively. Pose-supervised pretraining configurations obtain lower errors than CSI-only JEPA, and enabling the trained residual refiner reduces overall MPJPE by 13.8-15.6% across the evaluated configurations.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.07025 — 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 2026-10-07T12:37:32.843Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).