suxi123/Human-Art dataset hosted on Hugging Face and contributed by the HF Datasets community

Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19th century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.

Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted selectively. This is true even for the now automated estimation of human poses, as domain-specific, sufficiently large data sets required for training computational models are either not publicly available or not indexed at a fine enough granularity. With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19th century. A total of 10,749 human figures are precisely enclosed by rectangular bounding boxes, with a maximum of four per image labeled by up to 17 keypoints; among these are mainly joints such as elbows and knees. For machine learning purposes, the data set is divided into three subsets—training, validation, and testing—, that follow the established JSON-based Microsoft COCO format, respectively. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.

Dataset

This dataset was created by DDker

Contents

This dataset supports the doctoral dissertation The AI of the Beholder: A Quantitative Study on Human Perception and Appraisal of AI-Generated Images by Joshua Cunningham (Robert Morris University, 2025). The study investigates how individuals perceive and appraise artwork generated by artificial intelligence (AI) in comparison to human-created pieces. Specifically, it examines: (1) whether participants can accurately distinguish AI-generated from human-created artwork, (2) how age and exposure to AI art influence this ability and related appraisals, and (3) how digital versus traditional visual styles of AI art are perceived. The dataset includes anonymized survey responses collected from a diverse group of adult participants. Respondents were asked to evaluate a series of visual artworks—some created by humans, others by AI—across a range of styles, including both digital and traditional aesthetics. Additional demographic information such as age and prior exposure to AI tools was collected to assess moderating effects. The data were analyzed using SPSS to evaluate participant accuracy, preferences, and perceptions. This dataset can support further research into the psychological, aesthetic, and cultural dynamics of AI-generated content, as well as human-machine interaction in the creative arts.You may find the images used in this study, the original survey instrument, as well as a legend detailing each of the variables here: https://drive.google.com/drive/folders/125oaW82HpJUjI7EbaQWk_51puz0DgWKc?usp=sharing

Dataset

This dataset was created by Macayan, Piolo C.

Released under Apache 2.0

Contents

AI-ArtBench: An AI-generated Artistic Dataset AI-ArtBench is a dataset that contains 180,000+ art images. 60,000 of them are human-drawn art that was directly taken from ArtBench-10 dataset and the rest is generated equally using Latent Diffusion and Standard Diffusion models. The human-drawn art is in 256x256 resolution and images generated using Latent Diffusion and Standard Diffusion has 256x256 and 768x768 resolutions respectively.

Recently, AI-generated art improved significanlty that it has become increasingly harder now to differentiate between a real art and AI-generated art. This dataset can be used to train ML/DL models to identify AI-generated art. Further, since the dataset contains images from three different sources including, the dataset can also be used to train models to attribute art to its correct source verifying its authenticity.

The dataset was generated as a part of a research project in building a end-to-end system to detect and attribute AI-generated art images.

Reference This dataset was originally created for the study "ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style" by Silva et al.

Silva et al. (2024) 'ArtBrain: An Explainable end-to-end Toolkit for Classification and Attribution of AI-Generated Art and Style', arXiv. Available at: https://doi.org/10.48550/arXiv.2412.01512

The manuscript is under review at a journal and the reference will be updated here when the study is published.

Papers with Code Have you published a study on this dataset? Add your results to the Papers with Code page! https://paperswithcode.com/paper/artbrain-an-explainable-end-to-end-toolkit

People-Art is an object detection dataset which consists of people in 43 different styles. People contained in this dataset are quite different from those in common photographs. There are 42 categories of art styles and movements including Naturalism, Cubism, Socialist Realism, Impressionism, and Suprematism

Recent advancement in Artificial Intelligence (AI) has rendered image-synthesis models capable of producing complex artworks that appear nearly indistinguishable from human-made works. Here we present a quantitative assessment of human perception and preference for art generated by OpenAI’s DALL·E 2, a leading AI tool for art creation. Participants were presented with pairs of artworks, one human-made and one AI-generated, in either a preference-choice task or an origin-discrimination task. Results revealed a significant preference for AI-generated artworks. At the same time, a separate group of participants were above-chance at detecting which artwork within the pair was generated by AI, indicating a perceptible distinction between human and artificial creative works. These results raise questions about how a shift in art preference to favour synthetic creations might impact the way we think about art and its value to human society, prompting reflections on authorship, authenticity, and human creativity in the era of generative AI.

The AI Art and Painting Generator market is experiencing explosive growth, driven by advancements in generative AI models and increasing accessibility of these tools to both professional artists and hobbyists. While precise market size data for 2025 is unavailable, considering the rapid adoption and significant investments in this sector, a reasonable estimate for the 2025 market size could be $500 million. Assuming a Compound Annual Growth Rate (CAGR) of 30% (a conservative estimate given the current market dynamism), the market is projected to reach approximately $2.5 billion by 2033. This substantial growth is fueled by several key factors. The continuous improvement in algorithms allows for the creation of increasingly realistic and unique artwork, blurring the lines between human and machine creativity. Furthermore, the decreasing cost and accessibility of AI art generation tools are democratizing art creation, making it available to a wider audience than ever before. Ease of use, coupled with innovative features like style transfer and prompt engineering, is further expanding the market's appeal. However, the market also faces certain restraints. Concerns around copyright infringement and the ethical implications of AI-generated art need careful consideration and industry-wide solutions. Questions regarding ownership and the potential displacement of human artists remain significant challenges that will need to be addressed as the market matures. Despite these challenges, the overall market outlook remains highly positive, driven by consistent technological advancements, increasing user adoption, and the potential for diverse applications beyond fine art, extending into commercial design, marketing materials, and even personalized artistic experiences. Companies like Midjourney, Stable Diffusion, and OpenAI are leading the charge, shaping the future of digital art creation and influencing the landscape of this rapidly expanding market.

Dataset Card for people-in-paintings

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

people-in-paintings

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure





  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/people-in-paintings.

Explore the historical Whois records related to human-art.shop (Domain). Get insights into ownership history and changes over time.

AI in Art Creation Market Outlook

As per our latest research, the AI in Art Creation market size reached USD 1.82 billion globally in 2024, reflecting a robust expansion driven by the fusion of artificial intelligence and creative processes. The market is witnessing a remarkable compound annual growth rate (CAGR) of 23.7% from 2025 to 2033, positioning it to attain a value of approximately USD 14.22 billion by the end of the forecast period. This surge is primarily fueled by the increasing adoption of AI-powered tools across various creative domains, the democratization of art creation through accessible platforms, and the growing integration of AI in professional and educational art environments.

Several growth factors are propelling the AI in Art Creation market forward. First and foremost, the rapid advancements in machine learning algorithms and neural networks have significantly enhanced the capabilities of AI-powered art creation tools. These technologies now enable the generation of highly sophisticated, unique, and customizable artworks across multiple mediums, including digital painting, graphic design, animation, and music composition. The accessibility of these tools has lowered the entry barriers for aspiring artists and professionals alike, fostering a new wave of creative expression and innovation. Furthermore, the proliferation of user-friendly AI software and cloud-based platforms has made it easier for both individuals and organizations to experiment with and adopt AI-driven art solutions, further stimulating market growth.

Another critical driver is the increasing commercialization and monetization opportunities associated with AI-generated art. Art studios, advertising agencies, and entertainment companies are leveraging AI to streamline creative workflows, reduce production costs, and accelerate content creation timelines. AI-powered automation allows for rapid prototyping, real-time editing, and the generation of multiple creative iterations, which is particularly valuable in fast-paced industries such as digital marketing and media production. Additionally, the integration of AI in art education is equipping the next generation of artists with advanced tools and skills, ensuring a steady pipeline of talent and innovation in the market. The convergence of AI with emerging technologies such as augmented reality (AR) and virtual reality (VR) is also expanding the horizons of artistic expression and audience engagement.

The AI in Art Creation market is also benefiting from increased investments and collaborations between technology providers, creative professionals, and academic institutions. Governments and private organizations are funding research and development initiatives aimed at enhancing AI’s creative potential and addressing ethical considerations related to authorship, originality, and copyright. This collaborative ecosystem is fostering the development of innovative AI solutions tailored to the unique needs of artists, studios, and educational institutions. However, the market's growth trajectory will depend on the industry's ability to balance technological advancement with the preservation of artistic integrity and human creativity.

From a regional perspective, North America remains the dominant market for AI in Art Creation, driven by a mature technology infrastructure, a large pool of creative professionals, and significant investments in AI research and development. Europe follows closely, with a strong emphasis on digital innovation and cultural heritage preservation. The Asia Pacific region is emerging as a high-growth market, fueled by rising digital adoption, expanding creative industries, and supportive government initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local artists and organizations increasingly explore the potential of AI-powered art solutions. Overall, the global landscape is characterized by diverse adoption patterns and evolving regulatory frameworks, shaping the future of AI in Art Creation.

Dataset Card for MPII Human Pose

MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset.

The AI in art market is experiencing explosive growth, driven by advancements in generative AI models and increasing accessibility of powerful tools for artists and businesses alike. While precise market size figures for 2025 aren't provided, considering the rapid adoption of AI art generators and the substantial investments in this sector, a reasonable estimate for the 2025 market size could be placed at $500 million. This is based on observed growth in related sectors like AI software and the significant media attention surrounding AI art creation. Assuming a conservative Compound Annual Growth Rate (CAGR) of 35% — reflective of the rapid technological advancements and increasing market penetration — the market is projected to reach approximately $3.8 billion by 2033. This growth is fueled by several key factors. Firstly, the democratization of art creation through user-friendly platforms lowers the barrier to entry for aspiring artists and businesses. Secondly, the unique styles and creative possibilities offered by AI art are attracting a diverse range of users, from hobbyists to professionals seeking innovative solutions for content creation. Thirdly, the increasing integration of AI art tools within established design workflows is driving adoption across various industries, including advertising, gaming, and fashion. However, challenges such as ethical concerns regarding copyright and originality, along with the potential displacement of human artists, are acting as restraints on market growth. These concerns need to be addressed through responsible development and ethical guidelines within the industry. The market segmentation reveals a significant split between on-cloud and on-premise solutions, with the on-cloud segment currently dominating due to its accessibility and scalability. The application segment is bifurcated between personal and business use, with both showing strong growth potential. The geographical distribution shows North America and Europe as leading markets, primarily due to higher technological adoption and a more established digital art scene. However, rapid growth is expected in the Asia-Pacific region, driven by increasing internet penetration and a burgeoning creative industry. Continued innovation in AI models, coupled with addressing ethical concerns, will be crucial to sustaining the market’s impressive trajectory. The development of new AI-driven art forms and applications will further expand the market's scope and solidify its position as a transformative force within the creative landscape.

AI Vs Human 2.0

## Overview

AI Vs Human 2.0 is a dataset for classification tasks - it contains AI annotations for 2,090 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global human skin painting materials market is experiencing robust growth, driven by the increasing popularity of body art, theatrical performances, and special effects in film and television. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising demand for professional-grade, high-quality products from artists and makeup professionals is significantly contributing to market expansion. Secondly, the increasing adoption of temporary tattoos and body painting for events such as festivals, concerts, and Halloween celebrations is driving demand. Technological advancements in product formulation, resulting in improved pigmentation, longevity, and skin-friendliness, further contribute to market growth. Segmentation analysis shows that the art institutes and individual creators segments are leading the demand, with a preference for liquid formulations due to their ease of application and blending capabilities. Geographic regions like North America and Europe are currently major markets, but the Asia-Pacific region is expected to witness significant growth in the coming years due to rising disposable incomes and changing consumer preferences. However, factors such as potential skin allergies and regulatory hurdles related to product safety could pose challenges to market expansion. The market's trajectory suggests a continuous upward trend, propelled by the aforementioned drivers. While restraints such as safety concerns and potential regulatory changes need careful consideration, the overall positive market sentiment and diverse applications suggest a promising outlook for the next decade. The increasing professionalization of body art and the growing integration of special effects in entertainment are key factors to expect further growth and innovation within the sector, leading to more sophisticated and specialized products in the future. The projected CAGR of 7% indicates a substantial market expansion, making this sector an attractive investment opportunity for businesses involved in the production and distribution of human skin painting materials.

A survey fielded in the United States in April 2023 found that ** percent of respondents aged 65 and higher thought that images and videos that have been created by artificial intelligence should not be considered art because they are not made by humans. The attitude to calling AI-creations art balances out, as we get to younger age groups. For example, among 18-to-34-year-olds ** percent shared this opinion while ** percent did in fact consider visual media made with help of AI to be art.

Abstract This study investigates the course of the transformations of violence present in the human-animal relationship in multiple periods of time, using artistic expressions to contribute to this theme. The study uses a multidisciplinary approach from the fields of Arts, History, and Philosophy seeking contributions to the literature on marketing and consumer behavior. Visual critical analysis is the methodology used to analyze six works of art from different historical periods and to understand the human-animal relationship over time. Findings suggest that forms of violence are present in the transforming human-animal relationship. The research challenges the marketing domain of the discussion about this relationship, limited to positive experiences with companion animals; it brings interpretations of the ways of violence in the human-animal relationship over time to understand the meanings and practices of the present; adds elements aimed at discussions and reflections necessary for researchers and marketing professionals, on the human-animal relationship and violence.

In a survey conducted in South Korea in April 2023 on the perception to comparison of artworks made by artificial intelligence (AI) and human, ** percent of respondents answered that human-made will be better than AI-made for the impression to people, while ***** percent responded that AI will be better. Given the answers, respondents has a tendency to evaluate human-made could be better than AI-made in general, except technical level ability.