OverviewThis is a set of synthetic overhead imagery of wind turbines that was created with CityEngine. There are corresponding labels that provide the class, x and y coordinates, and height and width (YOLOv3 format) of the ground truth bounding boxes for each wind turbine in the images. These labels are named similarly to the images (e.g. image.png will have the label titled image.txt)..UseThis dataset is meant as supplementation to training an object detection model on overhead images of wind turbines. It can be added to the training set of an object detection model to potentially improve performance when using the model on real overhead images of wind turbines.WhyThis dataset was created to examine the utility of adding synthetic imagery to the training set of an object detection model to improve performance on rare objects. Since wind turbines are both very rare in number and sparse, this makes acquiring data very costly. This synthetic imagery is meant to solve this issue by automating the generation of new training data. The use of synthetic imagery can also be applied to the issue of cross-domain testing, where the model lacks training data on a particular region and consequently struggles when used on that region.MethodThe process for creating the dataset involved selecting background images from NAIP imagery available on Earth OnDemand. These images were randomlyselected from these geographies: forest, farmland, grasslands, water, urban/suburban,mountains, and deserts. No consideration was put into whether the background images would seem realistic. This is because we wanted to see if this would help the model become better at detecting wind turbines regardless of their context (which would help when using the model on novel geographies). Then, a script was used to select these at random and uniformly generate 3D models of large wind turbines over the image and then position the virtual camera to save four 608x608 pixel images. This process was repeated with the same random seed, but with no background image and the wind turbines colored as black. Next, these black and white images were converted into ground truth labels by grouping the black pixels in the images.

Synthetic Data Generation Market Size

As per the latest insights from Market.us, the Global Synthetic Data Generation Market is set to reach USD 6,637.98 million by 2034, expanding at a CAGR of 35.7% from 2025 to 2034. The market, valued at USD 313.50 million in 2024, is witnessing rapid growth due to rising demand for high-quality, privacy-compliant, and AI-driven data solutions.

North America dominated in 2024, securing over 35% of the market, with revenues surpassing USD 109.7 million. The region’s leadership is fueled by strong investments in artificial intelligence, machine learning, and data security across industries such as healthcare, finance, and autonomous systems. With increasing reliance on synthetic data to enhance AI model training and reduce data privacy risks, the market is poised for significant expansion in the coming years.

Synthetic Data Generation Market size was valued at USD 0.4 Billion in 2024 and is projected to reach USD 9.3 Billion by 2032, growing at a CAGR of 46.5 % from 2026 to 2032.

The Synthetic Data Generation Market is driven by the rising demand for AI and machine learning, where high-quality, privacy-compliant data is crucial for model training. Businesses seek synthetic data to overcome real-data limitations, ensuring security, diversity, and scalability without regulatory concerns. Industries like healthcare, finance, and autonomous vehicles increasingly adopt synthetic data to enhance AI accuracy while complying with stringent privacy laws.

Additionally, cost efficiency and faster data availability fuel market growth, reducing dependency on expensive, time-consuming real-world data collection. Advancements in generative AI, deep learning, and simulation technologies further accelerate adoption, enabling realistic synthetic datasets for robust AI model development.

The Synthetic Data Generation Market size is expected to reach a valuation of USD 36.09 Billion in 2033 growing at a CAGR of 39.45%. The research report classifies market by share, trend, demand and based on segmentation by Data Type, Modeling Type, Offering, Application, End Use and Regional Outloo...

Synthetic Data Generation Market Outlook

According to our latest research, the global synthetic data generation market size reached USD 1.6 billion in 2024, demonstrating robust expansion driven by increasing demand for high-quality, privacy-preserving datasets. The market is projected to grow at a CAGR of 38.2% over the forecast period, reaching USD 19.2 billion by 2033. This remarkable growth trajectory is fueled by the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, coupled with stringent data privacy regulations that necessitate innovative data solutions. As per our latest research, organizations worldwide are increasingly leveraging synthetic data to address data scarcity, enhance AI model training, and ensure compliance with evolving privacy standards.

One of the primary growth factors for the synthetic data generation market is the rising emphasis on data privacy and regulatory compliance. With the implementation of stringent data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, enterprises are under immense pressure to safeguard sensitive information. Synthetic data offers a compelling solution by enabling organizations to generate artificial datasets that mirror the statistical properties of real data without exposing personally identifiable information. This not only facilitates regulatory compliance but also empowers organizations to innovate without the risk of data breaches or privacy violations. As businesses increasingly recognize the value of privacy-preserving data, the demand for advanced synthetic data generation solutions is set to surge.

Another significant driver is the exponential growth in AI and ML adoption across various sectors, including healthcare, finance, automotive, and retail. High-quality, diverse, and unbiased data is the cornerstone of effective AI model development. However, acquiring such data is often challenging due to privacy concerns, limited availability, or high acquisition costs. Synthetic data generation bridges this gap by providing scalable, customizable datasets tailored to specific use cases, thereby accelerating AI training and reducing dependency on real-world data. Organizations are leveraging synthetic data to enhance algorithm performance, mitigate data bias, and simulate rare events, which are otherwise difficult to capture in real datasets. This capability is particularly valuable in sectors like autonomous vehicles, where training models on rare but critical scenarios is essential for safety and reliability.

Furthermore, the growing complexity of data types—ranging from tabular and image data to text, audio, and video—has amplified the need for versatile synthetic data generation tools. Enterprises are increasingly seeking solutions that can generate multi-modal synthetic datasets to support diverse applications such as fraud detection, product testing, and quality assurance. The flexibility offered by synthetic data generation platforms enables organizations to simulate a wide array of scenarios, test software systems, and validate AI models in controlled environments. This not only enhances operational efficiency but also drives innovation by enabling rapid prototyping and experimentation. As the digital ecosystem continues to evolve, the ability to generate synthetic data across various formats will be a critical differentiator for businesses striving to maintain a competitive edge.

Regionally, North America leads the synthetic data generation market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the strong presence of technology giants, advanced research institutions, and a favorable regulatory environment that encourages AI innovation. Europe is witnessing rapid growth due to proactive data privacy regulations and increasing investments in digital transformation initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the proliferation of digital technologies and rising adoption of AI-powered solutions across industries. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by government-led digitalization programs and expanding IT infrastructure.

This data-set is a supplementary material related to the generation of synthetic images of a corridor in the University of Melbourne, Australia from a building information model (BIM). This data-set was generated to check the ability of deep learning algorithms to learn task of indoor localisation from synthetic images, when being tested on real images. =============================================================================The following is the name convention used for the data-sets. The brackets show the number of images in the data-set.REAL DATAReal
---------------------> Real images (949 images)

Gradmag-Real -------> Gradmag of real data (949 images)SYNTHETIC DATASyn-Car
----------------> Cartoonish images (2500 images)

Syn-pho-real ----------> Synthetic photo-realistic images (2500 images)

Syn-pho-real-tex -----> Synthetic photo-realistic textured (2500 images)

Syn-Edge --------------> Edge render images (2500 images)

Gradmag-Syn-Car ---> Gradmag of Cartoonish images (2500 images)=============================================================================Each folder contains the images and their respective groundtruth poses in the following format [ImageName X Y Z w p q r].To generate the synthetic data-set, we define a trajectory in the 3D indoor model. The points in the trajectory serve as the ground truth poses of the synthetic images. The height of the trajectory was kept in the range of 1.5–1.8 m from the floor, which is the usual height of holding a camera in hand. Artificial point light sources were placed to illuminate the corridor (except for Edge render images). The length of the trajectory was approximately 30 m. A virtual camera was moved along the trajectory to render four different sets of synthetic images in Blender*. The intrinsic parameters of the virtual camera were kept identical to the real camera (VGA resolution, focal length of 3.5 mm, no distortion modeled). We have rendered images along the trajectory at 0.05 m interval and ± 10° tilt.The main difference between the cartoonish (Syn-car) and photo-realistic images (Syn-pho-real) is the model of rendering. Photo-realistic rendering is a physics-based model that traces the path of light rays in the scene, which is similar to the real world, whereas the cartoonish rendering roughly traces the path of light rays. The photorealistic textured images (Syn-pho-real-tex) were rendered by adding repeating synthetic textures to the 3D indoor model, such as the textures of brick, carpet and wooden ceiling. The realism of the photo-realistic rendering comes at the cost of rendering times. However, the rendering times of the photo-realistic data-sets were considerably reduced with the help of a GPU. Note that the naming convention used for the data-sets (e.g. Cartoonish) is according to Blender terminology.An additional data-set (Gradmag-Syn-car) was derived from the cartoonish images by taking the edge gradient magnitude of the images and suppressing weak edges below a threshold. The edge rendered images (Syn-edge) were generated by rendering only the edges of the 3D indoor model, without taking into account the lighting conditions. This data-set is similar to the Gradmag-Syn-car data-set, however, does not contain the effect of illumination of the scene, such as reflections and shadows.*Blender is an open-source 3D computer graphics software and finds its applications in video games, animated films, simulation and visual art. For more information please visit: http://www.blender.orgPlease cite the papers if you use the data-set:1) Acharya, D., Khoshelham, K., and Winter, S., 2019. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. ISPRS Journal of Photogrammetry and Remote Sensing. 150: 245-258.2) Acharya, D., Singha Roy, S., Khoshelham, K. and Winter, S. 2019. Modelling uncertainty of single image indoor localisation using a 3D model and deep learning. In ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, IV-2/W5, pages 247-254.

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global Synthetic Data Generation Engine market size reached USD 1.42 billion in 2024, reflecting a rapidly expanding sector driven by the escalating demand for advanced data solutions. The market is expected to achieve a robust CAGR of 37.8% from 2025 to 2033, propelling it to an estimated value of USD 21.8 billion by 2033. This exceptional growth is primarily fueled by the increasing need for high-quality, privacy-compliant datasets to train artificial intelligence and machine learning models in sectors such as healthcare, BFSI, and IT & telecommunications. As per our latest research, the proliferation of data-centric applications and stringent data privacy regulations are acting as significant catalysts for the adoption of synthetic data generation engines globally.

One of the key growth factors for the synthetic data generation engine market is the mounting emphasis on data privacy and compliance with regulations such as GDPR and CCPA. Organizations are under immense pressure to protect sensitive customer information while still deriving actionable insights from data. Synthetic data generation engines offer a compelling solution by creating artificial datasets that mimic real-world data without exposing personally identifiable information. This not only ensures compliance but also enables organizations to accelerate their AI and analytics initiatives without the constraints of data access or privacy risks. The rising awareness among enterprises about the benefits of synthetic data in mitigating data breaches and regulatory penalties is further propelling market expansion.

Another significant driver is the exponential growth in artificial intelligence and machine learning adoption across industries. Training robust and unbiased models requires vast and diverse datasets, which are often difficult to obtain due to privacy concerns, labeling costs, or data scarcity. Synthetic data generation engines address this challenge by providing scalable and customizable datasets for various applications, including machine learning model training, data augmentation, and fraud detection. The ability to generate balanced and representative data has become a critical enabler for organizations seeking to improve model accuracy, reduce bias, and accelerate time-to-market for AI solutions. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where data diversity and privacy are paramount.

Furthermore, the increasing complexity of data types and the need for multi-modal data synthesis are shaping the evolution of the synthetic data generation engine market. With the proliferation of unstructured data in the form of images, videos, audio, and text, organizations are seeking advanced engines capable of generating synthetic data across multiple modalities. This capability enhances the versatility of synthetic data solutions, enabling their application in emerging use cases such as autonomous vehicle simulation, natural language processing, and biometric authentication. The integration of generative AI techniques, such as GANs and diffusion models, is further enhancing the realism and utility of synthetic datasets, expanding the addressable market for synthetic data generation engines.

From a regional perspective, North America continues to dominate the synthetic data generation engine market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the strong presence of technology giants, early adoption of AI and machine learning, and stringent regulatory frameworks. Europe follows closely, driven by robust data privacy regulations and increasing investments in digital transformation. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, supported by expanding IT infrastructure, government-led AI initiatives, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also witnessing gradual adoption, fueled by the growing recognition of synthetic data's potential to overcome data access and privacy challenges.

The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

Real vs Fake Image Dataset

Overview

This dataset consists of two primary categories: real_images and fake_images. The real_images category contains authentic images, while the fake_images category includes synthetic images generated using various advanced generative models. The purpose of this dataset is to facilitate research and development in the field of image classification, focusing on distinguishing between real and synthetic images.

Dataset Structure

The dataset is organized as follows:

fake_images

The fake_images folder contains synthetic images generated using various generative models. Each subfolder represents a specific image generation model:

• big_gan: Images generated using the BigGAN model.
• cips: Images generated by CIPS (Conditional Image Prior Sampling).
• ddpm: Images generated by Denoising Diffusion Probabilistic Models.
• denoising_diffusion_gan: Hybrid GAN and diffusion model.
• diffusion_gan: GANs using diffusion processes for image generation.
• face_synthetics: Synthetic face images generated using models like StyleGAN.
• gansformer: GAN-based transformer architecture for image synthesis.
• gau_gan: Images generated from sketches.
• generative_inpainting: Images generated via inpainting.
• glide: Text-to-image generative model.
• lama: Latent manifold-based image generation.
• latent_diffusion: Diffusion model operating in latent space.
• mat: Artistic texture generation model.
• palette: Colorful image generation model.
• projected_gan: GANs with projected approaches for quality improvements.
• sfhq: High-resolution synthetic facial images.
• stable_diffusion: Popular image generation using stable diffusion models.
• star_gan: Multi-domain image transformation.
• stylegan1: First version of the StyleGAN architecture.
• stylegan2: Improved version of StyleGAN.
• stylegan3: Latest version of StyleGAN with more stable and realistic output.
• taming_transformer: Transformer-based image generation.
• vq_diffusion: Model combining vector quantization with diffusion.

real_images

This folder contains authentic, real-world images, which are used as the ground truth for comparison with the generated fake_images.

Usage

This dataset can be used for training and evaluating image classification models, particularly those focused on distinguishing real images from synthetic ones. It is well-suited for experiments with generative adversarial networks (GANs), diffusion models, and other deep learning techniques.

This dataset contains synthetic and real images, with their labels, for Computer Vision in robotic surgery. It is part of ongoing research on sim-to-real applications in surgical robotics. The dataset will be updated with further details and references once the related work is published. For further information see the repository on GitHub: https://github.com/PietroLeoncini/Surgical-Synthetic-Data-Generation-and-Segmentation

Synthetic Medical Image Data Services Market Outlook

According to our latest research, the global synthetic medical image data services market size stood at USD 452 million in 2024, reflecting robust adoption across healthcare and life sciences sectors. The market is expected to grow at a remarkable CAGR of 33.7% from 2025 to 2033, reaching a projected value of USD 5.4 billion by 2033. This exponential growth is primarily driven by the escalating demand for high-quality, diverse, and annotated medical imaging datasets to power artificial intelligence (AI) and machine learning (ML) algorithms for diagnostics, research, and training purposes. As per our comprehensive analysis, the rapid integration of synthetic data solutions is revolutionizing medical imaging workflows, enabling healthcare stakeholders to overcome data scarcity and privacy concerns while accelerating innovation.

The synthetic medical image data services market is experiencing significant growth due to the increasing need for large, annotated datasets to train and validate AI-driven diagnostic tools. Traditional approaches to medical image acquisition are often hampered by regulatory restrictions, data privacy concerns, and the inherent variability and scarcity of rare disease cases. Synthetic data generation addresses these challenges by creating realistic, customizable, and privacy-compliant datasets that enhance the performance and generalizability of AI models. Furthermore, the adoption of synthetic data accelerates the development cycle for new imaging technologies and supports the validation of medical devices, fostering a more agile and innovative healthcare ecosystem. The growing sophistication of generative adversarial networks (GANs) and other deep learning techniques has further improved the realism and utility of synthetic images, making them increasingly indispensable for modern medical imaging applications.

Another key growth factor for the synthetic medical image data services market is the rising emphasis on data privacy and compliance with regulations such as HIPAA in the United States and GDPR in Europe. These regulations impose stringent requirements on the use and sharing of patient data, often limiting the availability of real-world medical images for research and commercial purposes. Synthetic data offers a compelling solution by generating de-identified datasets that closely mimic real patient data without exposing sensitive information. This not only facilitates collaborative research and cross-institutional projects but also enables companies to scale their AI development efforts globally without the risk of data breaches or legal repercussions. As the healthcare industry continues to prioritize patient confidentiality, the demand for synthetic data services is expected to surge.

The market is further propelled by the expanding applications of synthetic medical image data in education, training, and research. Medical professionals, students, and researchers increasingly rely on diverse and complex datasets to hone their diagnostic skills, test new hypotheses, and develop innovative imaging solutions. Synthetic data bridges the gap where real-world datasets are insufficient or unavailable, providing a cost-effective and scalable alternative for simulation-based training and validation. This capability is especially valuable in regions with limited access to advanced imaging resources or rare clinical cases. As academic and research institutions intensify their focus on AI and machine learning in healthcare, synthetic data services are poised to become a cornerstone of medical education and innovation.

From a regional perspective, North America currently leads the synthetic medical image data services market, accounting for the largest share due to its advanced healthcare infrastructure, strong presence of AI technology providers, and supportive regulatory environment. Europe follows closely, driven by robust investments in digital health and a proactive stance on data privacy. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, increasing healthcare expenditure, and a burgeoning ecosystem of AI startups. Latin America and the Middle East & Africa, while still nascent, are expected to witness accelerated adoption as healthcare modernization initiatives gain momentum. Overall, the global market landscape is characterized by dynamic growth opportunities, with both developed and emerging regions contributing to the expansion of synthetic medical image da

Multiturn Multimodal

We want to generate synthetic data that able to understand position and relationship between multi-images and multi-audio, example as below, All notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/multiturn-multimodal

  multi-images

synthetic-multi-images-relationship.jsonl, 100000 rows, 109MB. Images at https://huggingface.co/datasets/mesolitica/translated-LLaVA-Pretrain/tree/main

  Example data

{'filename':… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/synthetic-multiturn-multimodal.

Data scarcity presents a significant obstacle in the field of biomedicine, where acquiring diverse and sufficient datasets can be costly and challenging. Synthetic data generation offers a potential solution to this problem by expanding dataset sizes, thereby enabling the training of more robust and generalizable machine learning models. Although previous studies have explored synthetic data generation for cancer diagnosis, they have predominantly focused on single-modality settings, such as whole-slide image tiles or RNA-Seq data. To bridge this gap, we propose a novel approach, RNA-Cascaded-Diffusion-Model or RNA-CDM, for performing RNA-to-image synthesis in a multi-cancer context, drawing inspiration from successful text-to-image synthesis models used in natural images. In our approach, we employ a variational auto-encoder to reduce the dimensionality of a patient’s gene expression profile, effectively distinguishing between different types of cancer. Subsequently, we employ a cascaded diffusion model to synthesize realistic whole-slide image tiles using the latent representation derived from the patient’s RNA-Seq data. Our results demonstrate that the generated tiles accurately preserve the distribution of cell types observed in real-world data, with state-of-the-art cell identification models successfully detecting important cell types in the synthetic samples. Furthermore, we illustrate that the synthetic tiles maintain the cell fraction observed in bulk RNA-Seq data and that modifications in gene expression affect the composition of cell types in the synthetic tiles. Next, we utilize the synthetic data generated by RNA-CDM to pretrain machine learning models and observe improved performance compared to training from scratch. Our study emphasizes the potential usefulness of synthetic data in developing machine learning models in scarce-data settings, while also highlighting the possibility of imputing missing data modalities by leveraging the available information. In conclusion, our proposed RNA-CDM approach for synthetic data generation in biomedicine, particularly in the context of cancer diagnosis, offers a novel and promising solution to address data scarcity. By generating synthetic data that align with real-world distributions and leveraging it to pretrain machine learning models, we contribute to the development of robust clinical decision support systems and potential advancements in precision medicine.

Global Synthetic Data Generation Market was valued at USD 310 Million in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 30.4% through 2029F.

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy, escalating data security concerns, and the rising demand for high-quality training data for AI and machine learning models. The market's expansion is fueled by several key factors: the growing adoption of AI across various industries, the limitations of real-world data availability due to privacy regulations like GDPR and CCPA, and the cost-effectiveness and efficiency of synthetic data generation. We project a market size of approximately $2 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033). This rapid expansion is expected to continue, reaching an estimated market value of over $10 billion by 2033. The market is segmented based on deployment models (cloud, on-premise), data types (image, text, tabular), and industry verticals (healthcare, finance, automotive). Major players are actively investing in research and development, fostering innovation in synthetic data generation techniques and expanding their product offerings to cater to diverse industry needs. Competition is intense, with companies like AI.Reverie, Deep Vision Data, and Synthesis AI leading the charge with innovative solutions. However, several challenges remain, including ensuring the quality and fidelity of synthetic data, addressing the ethical concerns surrounding its use, and the need for standardization across platforms. Despite these challenges, the market is poised for significant growth, driven by the ever-increasing need for large, high-quality datasets to fuel advancements in artificial intelligence and machine learning. The strategic partnerships and acquisitions in the market further accelerate the innovation and adoption of synthetic data platforms. The ability to generate synthetic data tailored to specific business problems, combined with the increasing awareness of data privacy issues, is firmly establishing synthetic data as a key component of the future of data management and AI development.

Ray tracin based image generation methodology to render realistic images of particle image velocimetry (PIV) and background oriented schlieren (BOS) experiments in the presence of density/refractive index gradients.

Artificial Intelligence-based image generation has recently seen remarkable advancements, largely driven by deep learning techniques, such as Generative Adversarial Networks (GANs). With the influx and development of generative models, so too have biometric re-identification models and presentation attack detection models seen a surge in discriminative performance. However, despite the impressive photo-realism of generated samples and the additive value to the data augmentation pipeline, the role and usage of machine learning models has received intense scrutiny and criticism, especially in the context of biometrics, often being labeled as untrustworthy. Problems that have garnered attention in modern machine learning include: humans' and machines' shared inability to verify the authenticity of (biometric) data, the inadvertent leaking of private biometric data through the image synthesis process, and racial bias in facial recognition algorithms. Given the arrival of these unwanted side effects, public trust has been shaken in the blind use and ubiquity of machine learning.

However, in tandem with the advancement of generative AI, there are research efforts to re-establish trust in generative and discriminative machine learning models. Explainability methods based on aggregate model salience maps can elucidate the inner workings of a detection model, establishing trust in a post hoc manner. The CYBORG training strategy, originally proposed by Boyd, attempts to actively build trust into discriminative models by incorporating human salience into the training process.

In doing so, CYBORG-trained machine learning models behave more similar to human annotators and generalize well to unseen types of synthetic data. Work in this dissertation also attempts to renew trust in generative models by training generative models on synthetic data in order to avoid identity leakage in models trained on authentic data. In this way, the privacy of individuals whose biometric data was seen during training is not compromised through the image synthesis procedure. Future development of privacy-aware image generation techniques will hopefully achieve the same degree of biometric utility in generative models with added guarantees of trustworthiness.

The Artificial Intelligence (AI) Synthetic Data Service market is experiencing rapid growth, driven by the increasing need for high-quality data to train and validate AI models, especially in sectors with data scarcity or privacy concerns. The market, estimated at $2 billion in 2025, is projected to expand significantly over the next decade, achieving a Compound Annual Growth Rate (CAGR) of approximately 30% from 2025 to 2033. This robust growth is fueled by several key factors: the escalating adoption of AI across various industries, the rising demand for robust and unbiased AI models, and the growing awareness of data privacy regulations like GDPR, which restrict the use of real-world data. Furthermore, advancements in synthetic data generation techniques, enabling the creation of more realistic and diverse datasets, are accelerating market expansion. Major players like Synthesis, Datagen, Rendered, Parallel Domain, Anyverse, and Cognata are actively shaping the market landscape through innovative solutions and strategic partnerships. The market is segmented by data type (image, text, time-series, etc.), application (autonomous driving, healthcare, finance, etc.), and deployment model (cloud, on-premise). Despite the significant growth potential, certain restraints exist. The high cost of developing and deploying synthetic data generation solutions can be a barrier to entry for smaller companies. Additionally, ensuring the quality and realism of synthetic data remains a crucial challenge, requiring continuous improvement in algorithms and validation techniques. Overcoming these limitations and fostering wider adoption will be key to unlocking the full potential of the AI Synthetic Data Service market. The historical period (2019-2024) likely saw a lower CAGR due to initial market development and technology maturation, before experiencing the accelerated growth projected for the forecast period (2025-2033). Future growth will heavily depend on further technological advancements, decreasing costs, and increasing industry awareness of the benefits of synthetic data.

Scripts for generating domain-randomised synthetic images with Omniverse Replicator and for training/evaluating a YOLOv8 detector in robotic manipulation tasks, reproducing all experiments reported in the paper.