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StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts — arXiv:2609.09890

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.09890
License (as archived): CC0-1.0
Archived: 2026-10-16T10:59:50.952Z
SHA-256 of the archived text: 5f5bf329d435df45d5fc6912fc652dd8acb98871b972bd334ed8075d21cd4ae5
Stable URL of this page: https://metraipolis.com/documento/dc_c142cb46fb74

How to cite

StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts — arXiv:2609.09890 — arXiv (en), https://arxiv.org/abs/2609.09890 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c142cb46fb74 (Metraipolis archive dc_c142cb46fb74, sha256 5f5bf329d435df45d5fc6912fc652dd8acb98871b972bd334ed8075d21cd4ae5)

BibTeX

@misc{dcc142cb46fb74,
  title = {StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts — arXiv:2609.09890},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.09890},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c142cb46fb74; sha256 5f5bf329d435df45d5fc6912fc652dd8acb98871b972bd334ed8075d21cd4ae5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.09890. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple outdoor environments (e.g., fields, courtyards). However, they often fail to maintain cross-view consistency under camera rotation, especially in structurally complex urban environments. Without explicit modeling of spherical correspondence across views, existing approaches tend to produce object duplication, structural distortion, and layout inconsistency. To address this limitation, we propose StreetDiff, a multi-view diffusion framework that explicitly enforces cross-view alignment during denoising. StreetDiff introduces a Panorama--Perspective Synergy design to decouple global layout reasoning from local detail synthesis, and incorporates a Panorama Alignment Module (PAM) that establishes spherical-projection-based attention constraints across views. By injecting structured alignment constraints without modifying the diffusion backbone, our framework achieves robust cross-view coherence in challenging urban street scene generation tasks. In addition, we construct Street360, a large-scale HDR multi-view urban panorama dataset. Extensive experiments demonstrate that StreetDiff significantly improves structural consistency and visual fidelity compared to prior multi-view diffusion generation methods.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-10 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.09890 — 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 2026-10-16T10:02:34.338Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).