mlsanityData to 7 Oct 2026

Paint it Black: Generating paintings from text descriptions

arXiv 2302.08808 ↗First posted 17 Feb 2023cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

Two distinct tasks - generating photorealistic pictures from given text prompts and transferring the style of a painting to a real image to make it appear as though it were done by an artist, have been addressed many times, and several approaches have been proposed to accomplish them. However, the intersection of these two, i.e., generating paintings from a given caption, is a relatively unexplored area with little data available. In this paper, we have explored two distinct strategies and have integrated them together. First strategy is to generate photorealistic images and then apply style transfer and the second strategy is to train an image generation model on real images with captions and then fine-tune it on captioned paintings later. These two models are evaluated using different metrics as well as a user study is conducted to get human feedback on the produced results.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

17 of 24 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
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5 more
  1. 2015Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Featurecited by 181
  2. 2016Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challengecited by 116
  3. 2019Semantic Object Accuracy for Generative Text-to-Image Synthesiscited by 89
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  5. 2017BAM! The Behance Artistic Media Dataset for Recognition Beyond Photographycited by 43

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2023Synthetic images aid the recognition of human-made art forgeriescited by 4
  2. 2024Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Surveycited by 1
  3. 2025Mimesis, Poiesis, and Imagination: Exploring Text-to-Image Generation of Biblical Narrativescited by 0