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CLIP-CLOP: CLIP-Guided Collage and Photomontage

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arxiv 2205.03146 v3 pith:UL2H7YIU submitted 2022-05-06 cs.CV cs.AI

Piotr Mirowski , Dylan Banarse , Mateusz Malinowski , Simon Osindero , Chrisantha Fernando This is my paper · ORCID
classification cs.CVcs.AI
keywords collagescreativeimagepatchesachieveadjustaestheticallowing
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The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art. Increasingly more sophisticated generators enhanced the artworks' realism and visual appearance, and creative prompt engineering enabled stylistic expression. Guided by an artist-in-the-loop ideal, we design a gradient-based generator to produce collages. It requires the human artist to curate libraries of image patches and to describe (with prompts) the whole image composition, with the option to manually adjust the patches' positions during generation, thereby allowing humans to reclaim some control of the process and achieve greater creative freedom. We explore the aesthetic potentials of high-resolution collages, and provide an open-source Google Colab as an artistic tool.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Empowering LLMs to Understand and Generate Complex Vector Graphics

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LLM4SVG adds learnable SVG tokens and SFT data so LLMs can generate and describe scalable vector graphics much better than general-purpose LLMs.

  2. SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation

    cs.CV 2024-11 conditional novelty 5.0 of 10

    SVGDreamer++ uses SAM-based hierarchical masks and adaptive path control to generate text-guided SVGs that are more editable and visually detailed.

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