# Understanding and Creating Art with AI: Review and Outlook

arXiv: https://arxiv.org/abs/2102.09109 · HTML: https://mlsanity.com/paper/2102.09109/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 147 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [ArtEmis: Affective Language for Visual Art](https://mlsanity.com/paper/2101.07396/) (2021)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018)
- [Recognizing Image Style](https://mlsanity.com/paper/1311.3715/) (2013)
- [A Dataset and Baselines for Visual Question Answering on Art](https://mlsanity.com/paper/2008.12520/) (2020)
- [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018)
- [Weakly Supervised Object Detection in Artworks](https://mlsanity.com/paper/1810.02569/) (2018)
- [Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning](https://mlsanity.com/paper/1903.02678/) (2019)
- [Biases in Generative Art -- A Causal Look from the Lens of Art History](https://mlsanity.com/paper/2010.13266/) (2020)
- [Detecting People in Artwork with CNNs](https://mlsanity.com/paper/1610.08871/) (2016)
- [Learning scale-variant and scale-invariant features for deep image classification](https://mlsanity.com/paper/1602.01255/) (2016)
- [Can Computers Create Art?](https://mlsanity.com/paper/1801.04486/) (2018)
- [Autonomy, Authenticity, Authorship and Intention in computer generated art](https://mlsanity.com/paper/1903.02166/) (2019)
- [A Data Set and a Convolutional Model for Iconography Classification in Paintings](https://mlsanity.com/paper/2010.11697/) (2020)
- [Iconographic Image Captioning for Artworks](https://mlsanity.com/paper/2102.03942/) (2021)
- [An analysis of the transfer learning of convolutional neural networks for artistic images](https://mlsanity.com/paper/2011.02727/) (2020)
- [Linking Art through Human Poses](https://mlsanity.com/paper/1907.03537/) (2019)
- [Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors](https://mlsanity.com/paper/2009.03807/) (2020)
- [Multitask Painting Categorization by Deep Multibranch Neural Network](https://mlsanity.com/paper/1812.08052/) (2018)
- [Visual Question Answering for Cultural Heritage](https://mlsanity.com/paper/2003.09853/) (2020)
- [Visual link retrieval and knowledge discovery in painting datasets](https://mlsanity.com/paper/2003.08476/) (2020)
- [Recognizing Characters in Art History Using Deep Learning](https://mlsanity.com/paper/2003.14171/) (2020)
- [DeepPainter: Painter Classification Using Deep Convolutional Autoencoders](https://mlsanity.com/paper/1711.08763/) (2017)

## Built on by (most cited first, 30 of 60)

- [Holistic Evaluation of Text-To-Image Models](https://mlsanity.com/paper/2311.04287/) (2023)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [A Survey of AI Text-to-Image and AI Text-to-Video Generators](https://mlsanity.com/paper/2311.06329/) (2023)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Sampling](https://mlsanity.com/paper/2310.01819/) (2023)
- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [InstructG2I: Synthesizing Images from Multimodal Attributed Graphs](https://mlsanity.com/paper/2410.07157/) (2024)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [When Creators Meet the Metaverse: A Survey on Computational Arts](https://mlsanity.com/paper/2111.13486/) (2021)
- [Benchmarking Deepart Detection](https://mlsanity.com/paper/2302.14475/) (2023)
- [ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations](https://mlsanity.com/paper/2306.08141/) (2023)
- [Exploring the Use of Abusive Generative AI Models on Civitai](https://mlsanity.com/paper/2407.12876/) (2024)
- [Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings](https://mlsanity.com/paper/2105.08190/) (2021)
- [ArtMentor: AI-Assisted Evaluation of Artworks to Explore Multimodal Large Language Models Capabilities](https://mlsanity.com/paper/2502.13832/) (2025)
- [FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation](https://mlsanity.com/paper/2410.18615/) (2024)
- [Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?](https://mlsanity.com/paper/2207.12101/) (2022)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination](https://mlsanity.com/paper/2311.15477/) (2023)
- [Is Writing Prompts Really Making Art?](https://mlsanity.com/paper/2301.13049/) (2023)
- [From paintbrush to pixel: A review of deep neural networks in AI-generated art](https://mlsanity.com/paper/2302.10913/) (2023)
- [Beyond Reality: The Pivotal Role of Generative AI in the Metaverse](https://mlsanity.com/paper/2308.06272/) (2023)
- [Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation](https://mlsanity.com/paper/2309.00216/) (2023)
- [Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Exploration](https://mlsanity.com/paper/2403.19174/) (2024)
- [Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity](https://mlsanity.com/paper/2504.14125/) (2025)
- [Benchmarking Vision-Language and Multimodal Large Language Models in Zero-shot and Few-shot Scenarios: A study on Christian Iconography](https://mlsanity.com/paper/2509.18839/) (2025)
- [Design of Unmanned Air Vehicles Using Transformer Surrogate Models](https://mlsanity.com/paper/2211.08138/) (2022)
- [Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)](https://mlsanity.com/paper/2310.05270/) (2023)
- [Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution](https://mlsanity.com/paper/2503.13531/) (2025)
- [Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset](https://mlsanity.com/paper/2507.08384/) (2025)
- [From Sound to Sight: Towards AI-authored Music Videos](https://mlsanity.com/paper/2509.00029/) (2025)

## Abstract (arXiv)

Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art, motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This paper provides an integrated review of two facets of AI and art: 1) AI is used for art analysis and employed on digitized artwork collections; 2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, computational aesthetics, etc. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.