Metraipolis › archive › dc_9be21b45f1e4

The RenAIssance: Caption-Guided Diffusion Models for Historical-Art Visual Media — arXiv:2611.10599

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.10599
License (as archived): CC0-1.0
Archived: 2026-11-10T18:42:38.733Z
SHA-256 of the archived text: 491e6c68c396bfbb4577aa67fd612d2b323f831b121e9a9166e96fcdf9424ac6
Stable URL of this page: https://metraipolis.com/documento/dc_9be21b45f1e4

How to cite

The RenAIssance: Caption-Guided Diffusion Models for Historical-Art Visual Media — arXiv:2611.10599 — arXiv (en), https://arxiv.org/abs/2611.10599 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9be21b45f1e4 (Metraipolis archive dc_9be21b45f1e4, sha256 491e6c68c396bfbb4577aa67fd612d2b323f831b121e9a9166e96fcdf9424ac6)

BibTeX

@misc{dc9be21b45f1e4,
  title = {The RenAIssance: Caption-Guided Diffusion Models for Historical-Art Visual Media — arXiv:2611.10599},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.10599},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9be21b45f1e4; sha256 491e6c68c396bfbb4577aa67fd612d2b323f831b121e9a9166e96fcdf9424ac6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.10599. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative AI can expand cultural collections into prompt-responsive visual media, but historical artworks rarely provide the paired descriptions needed to adapt modern image generators. We present The RenAIssance, a scalable pipeline that turns a collection of historical European artworks into a reusable text-to-image media resource. The pipeline generates scene-level captions with BLIP and uses the resulting approximately 15,000 image--text pairs from DEArt to train a compact LoRA adapter for Stable Diffusion v1.5. The adapter shifts generated images toward the historical-art distribution while retaining the base model's ability to render novel concepts. On historical and contemporary prompts of different lengths, it improves FID to the DEArt reference distribution in every setting with only a modest decrease in CLIP text--image similarity. Qualitative results show that the same adapter can render both period-appropriate subjects and contemporary concepts in a coherent historical visual language. By decoupling a reusable style representation from the base generator, the approach offers a practical building block for controllable historical-art media creation, interactive authoring, and rich-media experiences.

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); Machine Learning (cs.LG); Multimedia (cs.MM).
Pagina del lavoro: https://arxiv.org/abs/2611.10599 — 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 2026-11-10T18:37:37.596Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).