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GSExplorer: Interactive World Generation with a Persistent 3D Gaussian World State — arXiv:2611.01543

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01543
License (as archived): CC0-1.0
Archived: 2026-11-03T18:17:57.975Z
SHA-256 of the archived text: 4bd3a7267ac65fdc5da941952846bd2360e2745eeae83c5c7d511d8ef5783ef9
Stable URL of this page: https://metraipolis.com/documento/dc_32dfec72ea82

How to cite

GSExplorer: Interactive World Generation with a Persistent 3D Gaussian World State — arXiv:2611.01543 — arXiv (en), https://arxiv.org/abs/2611.01543 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_32dfec72ea82 (Metraipolis archive dc_32dfec72ea82, sha256 4bd3a7267ac65fdc5da941952846bd2360e2745eeae83c5c7d511d8ef5783ef9)

BibTeX

@misc{dc32dfec72ea82,
  title = {GSExplorer: Interactive World Generation with a Persistent 3D Gaussian World State — arXiv:2611.01543},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01543},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_32dfec72ea82; sha256 4bd3a7267ac65fdc5da941952846bd2360e2745eeae83c5c7d511d8ef5783ef9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01543. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Interactive world generation requires both accurate local control and stable scene consistency over long rollouts. Existing methods improve local generation through camera or geometry conditioning and extend historical context with memory mechanisms. However, long-horizon exploration remains challenging as target-view guidance becomes incomplete and inconsistencies accumulate across repeated scene updates. We present \textbf{GSExplorer}, a generation--reconstruction framework built around a persistent 3D Gaussian world state. At each step, the current 3DGS scene provides multimodal rendering conditions for a condition-driven video generator, while camera geometry is injected through a separate Relative Ray Encoding branch. Generated videos are then fed back to a SLAM-based reconstruction backend, where inconsistency filtering prevents conflicting content from corrupting the scene and cross-chunk bundle adjustment improves spatial alignment across generation steps. We further design a data-construction pipeline that reproduces realistic 3DGS degradation during scene extrapolation, yielding 293K training pairs of degraded multimodal renderings and real target videos. GSExplorer achieves the best overall performance among the compared methods on both single-step and three-step WorldScore evaluation, with strong camera control and consistency across both settings, including a Camera Control score of 99.07 in single-step extrapolation. On 481-frame leave-and-return rollouts, it also shows strong revisit consistency, demonstrating the value of maintaining a reliable 3D world state for continued exploration.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01543 — 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 2026-11-03T18:09:53.597Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).