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Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline — arXiv:2609.07085

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.07085
License (as archived): CC0-1.0
Archived: 2026-10-16T15:11:38.616Z
SHA-256 of the archived text: 34e47455701baa11dfca547ad44c5295c11919cb4f55442d0a1da5b4f02cf049
Stable URL of this page: https://metraipolis.com/documento/dc_cd264bdde488

How to cite

Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline — arXiv:2609.07085 — arXiv (en), https://arxiv.org/abs/2609.07085 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd264bdde488 (Metraipolis archive dc_cd264bdde488, sha256 34e47455701baa11dfca547ad44c5295c11919cb4f55442d0a1da5b4f02cf049)

BibTeX

@misc{dccd264bdde488,
  title = {Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline — arXiv:2609.07085},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.07085},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd264bdde488; sha256 34e47455701baa11dfca547ad44c5295c11919cb4f55442d0a1da5b4f02cf049},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.07085. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion models pre-trained on large-scale datasets served as 2D priors are used to solve the ill-posed task but suffer from color deviation and view inconsistency, which can be curbed by using diffusion models fine-tuned with 3D annotated data served as 3D priors. However, 3D priors lack high-frequency details, which cannot be solved by direct complementation with 2D priors in spatial domain for introducing erroneous low-frequency 2D prior guidance. In this paper, we revisit the characteristics of different diffusion priors from the frequency perspective. Based on our observations, we theoretically present a unified framework of hybrid optimization using multiple diffusion priors in frequency domain. Under this framework, we further propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild. Morpheus3D enhances 3D prior with high-pass image-prompt 2D prior guidance to reconstruct high-quality 3D objects while effectively suppressing view inconsistency, low-frequency color deviation, and high-frequency lacking problems. Both quantitative and qualitative experiments on the public and our collected datasets with complex textures show that our method exhibits significant improvements in generation quality.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.07085 — 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 2026-10-16T14:49:57.181Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).