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Less Is More: Balancing Positive and Negative Space in Visual Concept Blending — arXiv:2609.00476

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00476
License (as archived): CC0-1.0
Archived: 2026-10-17T06:51:58.686Z
SHA-256 of the archived text: 8175bceca86b5b8cf963c4f167f54fb0f542396429088b45e02c0ea2be4636dd
Stable URL of this page: https://metraipolis.com/documento/dc_50befe11d2cd

How to cite

Less Is More: Balancing Positive and Negative Space in Visual Concept Blending — arXiv:2609.00476 — arXiv (en), https://arxiv.org/abs/2609.00476 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_50befe11d2cd (Metraipolis archive dc_50befe11d2cd, sha256 8175bceca86b5b8cf963c4f167f54fb0f542396429088b45e02c0ea2be4636dd)

BibTeX

@misc{dc50befe11d2cd,
  title = {Less Is More: Balancing Positive and Negative Space in Visual Concept Blending — arXiv:2609.00476},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00476},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_50befe11d2cd; sha256 8175bceca86b5b8cf963c4f167f54fb0f542396429088b45e02c0ea2be4636dd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00476. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Graphic designers often blend visual concepts to communicate multiple ideas within a single image, leveraging positive and negative space to create balance, emphasis, and aesthetic appeal. While computational methods have begun to support automatic concept blending, they largely overlook the role of spatial composition in the design. To address this gap, we present an automatic pipeline that explicitly applies positive and negative space throughout the blending process. Our approach first identifies plausible regions for concept integration by combining semantic reasoning from vision-language models with geometric constraints derived from real-world examples. Conditioned on these regions, the system generates blended compositions using a hybrid pixel-vector pipeline: diffusion-based inpainting produces a fast, coarse initialization, which is then refined through vector-based optimization at the point level to ensure structural coherence and balanced semantic expression. A multimodal agent orchestrates this process as a planner and evaluator, enabling iterative improvement and interpretable control. Through an evaluation using both baseline comparisons and a user study, we demonstrate greater expressiveness, creativity, and concept recognizability by effectively leveraging positive and negative space. We further demonstrate the generalizability of our approach across diverse applications, including controllable image and infographic generation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.HC).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.00476 — 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 2026-10-17T06:41:35.422Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).