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UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation — arXiv:2611.02631

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02631
License (as archived): CC0-1.0
Archived: 2026-11-03T18:55:03.326Z
SHA-256 of the archived text: 70d88f98393490cc34e284d7afc6ef76d531d003eeca53337e00dfa896110979
Stable URL of this page: https://metraipolis.com/documento/dc_385fcccdb2f4

How to cite

UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation — arXiv:2611.02631 — arXiv (en), https://arxiv.org/abs/2611.02631 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_385fcccdb2f4 (Metraipolis archive dc_385fcccdb2f4, sha256 70d88f98393490cc34e284d7afc6ef76d531d003eeca53337e00dfa896110979)

BibTeX

@misc{dc385fcccdb2f4,
  title = {UniCross3D: Unified Cross-View and Cross-Domain Diffusion for Consistent Single-Image 3D Generation — arXiv:2611.02631},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02631},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_385fcccdb2f4; sha256 70d88f98393490cc34e284d7afc6ef76d531d003eeca53337e00dfa896110979},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02631. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamentally challenging due to inherent ambiguities such as occlusion, lighting variations, and texture-geometry entanglement. While recent diffusion-based generative models have significantly improved novel view synthesis, existing approaches suffer from two critical limitations: lack of cross-view geometric consistency and insufficient cross-domain semantic alignment. To address these issues, we introduce UniCross3D, a unified cross-view and cross-domain diffusion framework designed explicitly for consistent and physically coherent 3D generation. UniCross3D features two novel contributions: (1) a cross-view latent regularization that enforces cross-view geometric consistency across synthesized viewpoints by penalizing latent variance, and (2) a cross-domain mutual information objective grounded in the physics of image formation, explicitly aligning synthesized color and normal maps. Extensive experiments demonstrate that UniCross3D achieves significantly improved view consistency and semantic alignment over state-of-the-art methods and yields higher-fidelity reconstructions, particularly under challenging textures and ambiguous viewpoints.

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) ; Graphics (cs.GR).
Pagina del lavoro: https://arxiv.org/abs/2611.02631 — 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 2026-11-03T18:16:53.007Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).