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<p>This paper explores the intersection of artificial intelligence and art curation through a project developed for the 2023 Helsinki Art Biennial, "New Directions May Emerge." Utilizing the Helsinki Art Museum (HAM) collection, our work re-imagines the city of Helsinki through the lens of machine perception. We employ CLIP to represent works of the HAM collection and assign fictional public coordinates to indoor artworks based on cosine similarity to existing outdoor public art. For each of these new locations, we generate synthetic 360° art panoramas with Stable Diffusion. This process approximates real-world spatial composition, derived from urban panoramas’ depth map extracted through MiDas, as a structural guide via ControlNet, while the aesthetic and thematic content is informed by CLIP-Interrogator-generated textual descriptions of the original artworks. The result is a machine curation that merges the artwork with a re-imagined physical space, inviting to blur the distinctions between art, context, and the machine’s interpretation. The project is presented as an interactive, web-based installation, inviting users to navigate a re-imagined version of the city and its cultural heritage. This work addresses how contemporary machine learning can move beyond data management to become an active participant in curatorial practice, creating new forms of audience interaction and spatial storytelling.</p>

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AI art curation: re-imagining the city of Helsinki in occasion of its Biennial

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Ludovica Schaerf,
  • Pepe Ballesteros Zapata,
  • Valentine Bernasconi,
  • Iacopo Neri,
  • Dario Negueruela del Castillo

摘要

This paper explores the intersection of artificial intelligence and art curation through a project developed for the 2023 Helsinki Art Biennial, "New Directions May Emerge." Utilizing the Helsinki Art Museum (HAM) collection, our work re-imagines the city of Helsinki through the lens of machine perception. We employ CLIP to represent works of the HAM collection and assign fictional public coordinates to indoor artworks based on cosine similarity to existing outdoor public art. For each of these new locations, we generate synthetic 360° art panoramas with Stable Diffusion. This process approximates real-world spatial composition, derived from urban panoramas’ depth map extracted through MiDas, as a structural guide via ControlNet, while the aesthetic and thematic content is informed by CLIP-Interrogator-generated textual descriptions of the original artworks. The result is a machine curation that merges the artwork with a re-imagined physical space, inviting to blur the distinctions between art, context, and the machine’s interpretation. The project is presented as an interactive, web-based installation, inviting users to navigate a re-imagined version of the city and its cultural heritage. This work addresses how contemporary machine learning can move beyond data management to become an active participant in curatorial practice, creating new forms of audience interaction and spatial storytelling.