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EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals — arXiv:2608.14754

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14754
License (as archived): CC0-1.0
Archived: 2026-10-19T02:13:52.878Z
SHA-256 of the archived text: d3628f4de6350fd713df723a6f4319d28876f1201f761e69411109f3c9dd62ac
Stable URL of this page: https://metraipolis.com/documento/dc_04bbcd625db4

How to cite

EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals — arXiv:2608.14754 — arXiv (en), https://arxiv.org/abs/2608.14754 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_04bbcd625db4 (Metraipolis archive dc_04bbcd625db4, sha256 d3628f4de6350fd713df723a6f4319d28876f1201f761e69411109f3c9dd62ac)

BibTeX

@misc{dc04bbcd625db4,
  title = {EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals — arXiv:2608.14754},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14754},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_04bbcd625db4; sha256 d3628f4de6350fd713df723a6f4319d28876f1201f761e69411109f3c9dd62ac},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14754. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Human motion is governed by a hierarchical motor system where the brain provides high-level intentions and lower-level structures coordinate detailed dynamics. Existing brain-computer interfaces (BCIs) typically oversimplify this into constrained classification or low-dimensional control, failing to capture the richness of natural movement. Bridging this gap to achieve open-vocabulary, full-body motion synthesis remains challenging due to the substantial cross-modal divergence between sparse neural signals and high-dimensional kinematics, as well as the lack of large-scale paired EEG-motion datasets. To address this, we introduce EEG2MOTION, the first EEG-motion-text dataset for human motion synthesis, comprising nearly 20,000 paired samples across thousands of motions. Using this dataset, we first demonstrate via multimodal contrastive learning that non-invasive EEG embeddings can be effectively aligned with text, video, and motion representations to decode high-level semantics. We then propose EEG-conditioned Masked Motion Model (EMMM), a generative framework that unites an EEG encoder with a motion decoder to synthesize continuous, full-body human motions directly from brain activity. Experimental results show that EMMM generates coherent and realistic motion sequences from non-invasive brain signals. To the best of our knowledge, this is the first work to generate diverse full-body human motions from non-invasive brain signals, opening a new direction toward generative and open-vocabulary motor BCIs. See our project page: this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-18 in eess.SP).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.14754 — 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 2026-10-19T01:25:14.295Z dalla pagina https://arxiv.org/catchup/eess.SP/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).