A large scale synthetic dataset about dynamic human-object interactions. It features about 10 hours of video with 8337 sequences and 2M images. The generation of this dataset is described in the paper "InterTrack: Tracking Human Object Interaction without Object Templates" (3DV'25). Please check the github repo for detailed file structure of the dataset: https://github.com/xiexh20/ProciGen If you use our data, please cite: @inproceedings{xie2024InterTrack, title = {InterTrack: Tracking Human Object Interaction without Object Templates}, author = {Xie, Xianghui and Lenssen, Jan Eric and Pons-Moll, Gerard}, booktitle = {International Conference on 3D Vision (3DV)}, month = {March}, year = {2025}, }

Synthetic Data Video Generator Market Outlook

According to our latest research, the global synthetic data video generator market size reached USD 1.32 billion in 2024 and is anticipated to grow at a robust CAGR of 38.7% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 18.59 billion, driven by rapid advancements in artificial intelligence, the growing need for high-quality training data for machine learning models, and increasing adoption across industries such as autonomous vehicles, healthcare, and surveillance. The surge in demand for data privacy, coupled with the necessity to overcome data scarcity and bias in real-world datasets, is significantly fueling the synthetic data video generator market's growth trajectory.

One of the primary growth factors for the synthetic data video generator market is the escalating demand for high-fidelity, annotated video datasets required to train and validate AI-driven systems. Traditional data collection methods are often hampered by privacy concerns, high costs, and the sheer complexity of obtaining diverse and representative video samples. Synthetic data video generators address these challenges by enabling the creation of large-scale, customizable, and bias-free datasets that closely mimic real-world scenarios. This capability is particularly vital for sectors such as autonomous vehicles and robotics, where the accuracy and safety of AI models depend heavily on the quality and variety of training data. As organizations strive to accelerate innovation and reduce the risks associated with real-world data collection, the adoption of synthetic data video generation technologies is expected to expand rapidly.

Another significant driver for the synthetic data video generator market is the increasing regulatory scrutiny surrounding data privacy and compliance. With stricter regulations such as GDPR and CCPA coming into force, organizations face mounting challenges in using real-world video data that may contain personally identifiable information. Synthetic data offers an effective solution by generating video datasets devoid of any real individuals, thereby ensuring compliance while still enabling advanced analytics and machine learning. Moreover, synthetic data video generators empower businesses to simulate rare or hazardous events that are difficult or unethical to capture in real life, further enhancing model robustness and preparedness. This advantage is particularly pronounced in healthcare, surveillance, and automotive industries, where data privacy and safety are paramount.

Technological advancements and increasing integration with cloud-based platforms are also propelling the synthetic data video generator market forward. The proliferation of cloud computing has made it easier for organizations of all sizes to access scalable synthetic data generation tools without significant upfront investments in hardware or infrastructure. Furthermore, the continuous evolution of generative adversarial networks (GANs) and other deep learning techniques has dramatically improved the realism and utility of synthetic video data. As a result, companies are now able to generate highly realistic, scenario-specific video datasets at scale, reducing both the time and cost required for AI development. This democratization of synthetic data technology is expected to unlock new opportunities across a wide array of applications, from entertainment content production to advanced surveillance systems.

From a regional perspective, North America currently dominates the synthetic data video generator market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI technology providers, robust investment in research and development, and early adoption by automotive and healthcare sectors are key contributors to North America's market leadership. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increased focus on AI-driven innovation. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, expanding IT infrastructure, and increasing investments in autonomous systems and smart city projects. Latin America and Middle East & Africa, while still nascent, are expected to experience steady uptake as awareness and technological capabilities continue to grow.

Component Analysis

The synthetic data video generator market by comp

Synthetic Data Video Generator Market Outlook

According to our latest research, the global Synthetic Data Video Generator market size in 2024 stands at USD 1.46 billion, with robust momentum driven by advances in artificial intelligence and the increasing need for high-quality, privacy-compliant video datasets. The market is witnessing a remarkable compound annual growth rate (CAGR) of 37.2% from 2025 to 2033, propelled by growing adoption across sectors such as autonomous vehicles, healthcare, and surveillance. By 2033, the market is projected to reach USD 18.16 billion, reflecting a seismic shift in how organizations leverage synthetic data to accelerate innovation and mitigate data privacy concerns.

The primary growth factor for the Synthetic Data Video Generator market is the surging demand for data privacy and compliance in machine learning and computer vision applications. As regulatory frameworks like GDPR and CCPA become more stringent, organizations are increasingly wary of using real-world video data that may contain personally identifiable information. Synthetic data video generators provide a scalable and ethical alternative, enabling enterprises to train and validate AI models without risking privacy breaches. This trend is particularly pronounced in sectors such as healthcare and finance, where data sensitivity is paramount. The ability to generate diverse, customizable, and annotation-rich video datasets not only addresses compliance requirements but also accelerates the development and deployment of AI solutions.

Another significant driver is the rapid evolution of deep learning algorithms and simulation technologies, which have dramatically improved the realism and utility of synthetic video data. Innovations in generative adversarial networks (GANs), 3D rendering engines, and advanced simulation platforms have made it possible to create synthetic videos that closely mimic real-world environments and scenarios. This capability is invaluable for industries like autonomous vehicles and robotics, where extensive and varied training data is essential for safe and reliable system behavior. The reduction in time, cost, and logistical complexity associated with collecting and labeling real-world video data further enhances the attractiveness of synthetic data video generators, positioning them as a cornerstone technology for next-generation AI development.

The expanding use cases for synthetic video data across emerging applications also contribute to market growth. Beyond traditional domains such as surveillance and entertainment, synthetic data video generators are finding adoption in areas like augmented reality, smart retail, and advanced robotics. The flexibility to simulate rare, dangerous, or hard-to-capture scenarios offers a strategic advantage for organizations seeking to future-proof their AI initiatives. As synthetic data generation platforms become more accessible and user-friendly, small and medium enterprises are also entering the fray, democratizing access to high-quality training data and fueling a new wave of AI-driven innovation.

From a regional perspective, North America continues to dominate the Synthetic Data Video Generator market, benefiting from a concentration of technology giants, research institutions, and early adopters across key verticals. Europe follows closely, driven by strong regulatory emphasis on data protection and an active ecosystem of AI startups. Meanwhile, the Asia Pacific region is emerging as a high-growth market, buoyed by rapid digital transformation, government AI initiatives, and increasing investments in autonomous systems and smart cities. Latin America and the Middle East & Africa are also showing steady progress, albeit from a smaller base, as awareness and infrastructure for synthetic data generation mature.

Component Analysis

The Synthetic Data Video Generator market, when analyzed by component, is primarily segmented into Software and Services. The software segment currently commands the largest share, driven by the prolif

Videos created using prototypes and APIs for participatory research. The videos were used as technological probes presented to various stakeholders. This dataset was created in the context of Fact-Checking Chatbot Initiative. The proliferation of disinformation poses a significant challenge to societies. Within the field of journalism, fact-checking emerges as a critical tool to combat this issue. Technology has become a key enabler for the production and dissemination of dis- information. In this work, we question the use of technology as a solution to fight back disinformation, specifically examining the ethical implications of this choice. To address this, we organized a workshop using the Value Sensitive Design (VSD) methodology to explore questions in this context. The workshop introduced participants to the VSD framework, enabling them to critically assess whether specific scenarios align with human values, norms, and requirements. Real-world scenarios were discussed, including approaches implemented by legitimate news outlets and the use of 3D virtual characters by a Brazilian television employing deep learning. As artificial intelligence becomes more integrated into journalism, values such as truth, credibility, transparency, privacy, and consent become increasingly important considerations. Participants analyzed how technology impacts journalism values, norms, and practices, with a particular focus on aligning synthetic media technologies with automated fact-checking dissemination. In conclusion, the authors prepare a list of recommendations from valuable insights into the complex ethical considerations surrounding synthetic media technologies for automatic fact-checking dissemination. It also facilitated cross-border discussions, with 11 participants from seven countries engaging in fruitful dialogue on this vital topic. The study proposes evaluation criteria for AI-generated content in this diversity, including privacy protection, inclusiveness, transparency, beauty standards conformity, engagement, meaningfulness, and effortlessness.

We introduce Vident-synth, a large dataset of synthetic dental videos with corresponding ground truth forward and backward optical flows and occlusion masks. It can be used for:

The AI video generation tool market is experiencing explosive growth, driven by increasing demand for high-quality, cost-effective video content across various sectors. The market, estimated at $2 billion in 2025, is projected to exhibit a robust Compound Annual Growth Rate (CAGR) of 30% from 2025 to 2033, reaching an estimated market value of $15 billion by 2033. This surge is fueled by several key factors. Firstly, advancements in deep learning and artificial intelligence are enabling the creation of increasingly realistic and sophisticated videos with minimal human intervention. Secondly, the rising adoption of video content across marketing, education, and entertainment sectors is creating a massive demand for efficient and scalable video production solutions. Finally, the user-friendly interfaces of many AI video generation tools are lowering the barrier to entry for both individuals and businesses, fostering widespread adoption. However, challenges remain. High computational costs associated with training and deploying these AI models can represent a significant barrier to entry for smaller companies. Furthermore, concerns surrounding intellectual property rights, potential misuse for creating deepfakes, and the need for robust ethical guidelines are factors that could restrain market growth if not adequately addressed. Despite these challenges, the market's overall trajectory is positive, driven by continuous technological innovation and the expanding applications of AI video generation technology across diverse industries, including e-commerce, gaming, and virtual/augmented reality. Key players like OpenAI, Google, and Synthesia are leading the charge, continuously refining their products and expanding their market reach. The segmentation of the market reflects this diversity, with solutions catering to professional video editors, marketing teams, and individual content creators alike.

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 media software market is experiencing rapid growth, driven by increasing demand for realistic and engaging digital content across various sectors. The market, currently estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching a substantial market value. This robust growth is fueled by several key factors. Firstly, advancements in artificial intelligence (AI) and machine learning (ML) are leading to more sophisticated and cost-effective synthetic media creation tools. Secondly, the rising adoption of synthetic media in advertising, entertainment, and e-learning is significantly boosting market demand. Businesses are leveraging these tools for personalized marketing campaigns, immersive gaming experiences, and interactive educational content. Thirdly, the increasing availability of cloud-based solutions is making synthetic media technology more accessible to small and medium-sized enterprises (SMEs), further fueling market expansion. However, ethical concerns surrounding deepfakes and the potential for misuse of synthetic media remain significant restraints, requiring the development of robust verification and authentication technologies. The market segmentation reveals a strong preference for cloud-based solutions due to their scalability and cost-effectiveness. Large enterprises are leading the adoption, followed by a rapidly growing SME segment. Geographically, North America currently holds the largest market share, driven by early adoption and technological advancements. However, Asia-Pacific is poised for significant growth, fueled by expanding digital economies and increasing investment in AI and related technologies. Key players in the market, including Synthesia, ChatGPT, and Jasper, are continuously innovating and expanding their offerings to meet the evolving needs of diverse industries. Future market growth will hinge on addressing ethical concerns, enhancing the realism and quality of synthetic media, and expanding its applications across new sectors. The continued advancements in AI and ML are expected to further drive market expansion and innovation in the coming years.

9x9 views

Data used for our paper "WormSwin: Instance Segmentation of C. elegans using Vision Transformer".This publication is divided into three parts:

CSB-1 Dataset

Synthetic Images Dataset

MD Dataset

The CSB-1 Dataset consists of frames extracted from videos of Caenorhabditis elegans (C. elegans) annotated with binary masks. Each C. elegans is separately annotated, providing accurate annotations even for overlapping instances. All annotations are provided in binary mask format and as COCO Annotation JSON files (see COCO website).

The videos are named after the following pattern:

<"worm age in hours"_"mutation"_"irradiated (binary)"_"video index (zero based)">

For mutation the following values are possible:

wild type

csb-1 mutant

csb-1 with rescue mutation

An example video name would be 24_1_1_2 meaning it shows C. elegans with csb-1 mutation, being 24h old which got irradiated.

Video data was provided by M. Rieckher; Instance Segmentation Annotations were created under supervision of K. Bozek and M. Deserno.The Synthetic Images Dataset was created by cutting out C. elegans (foreground objects) from the CSB-1 Dataset and placing them randomly on background images also taken from the CSB-1 Dataset. Foreground objects were flipped, rotated and slightly blurred before placed on the background images.The same was done with the binary mask annotations taken from CSB-1 Dataset so that they match the foreground objects in the synthetic images. Additionally, we added rings of random color, size, thickness and position to the background images to simulate petri-dish edges.

This synthetic dataset was generated by M. Deserno.The Mating Dataset (MD) consists of 450 grayscale image patches of 1,012 x 1,012 px showing C. elegans with high overlap, crawling on a petri-dish.We took the patches from a 10 min. long video of size 3,036 x 3,036 px. The video was downsampled from 25 fps to 5 fps before selecting 50 random frames for annotating and patching.Like the other datasets, worms were annotated with binary masks and annotations are provided as COCO Annotation JSON files.

The video data was provided by X.-L. Chu; Instance Segmentation Annotations were created under supervision of K. Bozek and M. Deserno.

Further details about the datasets can be found in our paper.

Dataset Name

CGI synthetic videos generated in paper "Synthetic Video Enhances Physical Fidelity in Video Synthesis" (https://simulation.seaweed.video/)

  Dataset Overview

Number of samples: [uploading...] Annotations: [tags, captions] License: [apache-2.0] Citation: @article{zhao2025synthetic, title={Synthetic Video Enhances Physical Fidelity in Video Synthesis}, author={Zhao, Qi and Ni, Xingyu and Wang, Ziyu and Cheng, Feng and Yang, Ziyan and Jiang, Lu and Wang, Bohan}… See the full description on the dataset page: https://huggingface.co/datasets/kevinzzz8866/ByteDance_Synthetic_Videos.

The Synthetic Data is Segmented by Data Type (Tabular, Text/NLP, Image and Video, and More), Offering (Fully Synthetic, Partially Synthetic/Hybrid), Technology (GANs, Diffusion Models, and More), Deployment Mode (Cloud, On-Premise), Application (AI/ML Training and Development, and More), End User Industry (BFSI, Healthcare and Life-Sciences, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Artificial Intelligence in Video Market Outlook 2032

The global Artificial Intelligence in Video market size was USD 541.85 Million in 2023 and is likely to reach USD 3307.3 Million by 2032, expanding at a CAGR of 22.26 % during 2024–2032. The market growth is attributed to the growing advancements in machine learning and deep learning technologies and the increasing demand for personalized content.



Increasing demand for personalized content is expected to boost the global AI In Video market. AI enables the creation of interactive video content that adapts to the viewer's choices, providing a personalized and engaging viewing experience. Additionally, AI assists in creating personalized video content by automating certain aspects of the production process, such as editing and post-production. Thus, the rising demand for personalized content is propelling the market.

AI is being widely deployed for video content generation in marketing, education, and social media as it ensures consistency in video production, maintaining the same style, tone, and quality across multiple videos. Additionally, AI makes it possible to generate a large number of videos in a short amount of time, making it easier to scale video production. These benefits associated with AI in video generation increase their demand among vloggers and entertainment & media professionals.

Artificial Intelligence In Video Market Dynamics

The rise of Information Short Video content is becoming a significant trend in the AI-driven video market. These short videos, often lasting just a few minutes, are designed to convey information quickly and efficiently, catering to the fast-paced consumption habits of modern audiences. With the help of AI, creators can produce these videos at scale, ensuring that they are both engaging and info

Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.

Virtual KITTI contains 50 high-resolution monocular videos (21,260 frames) generated from five different virtual worlds in urban settings under different imaging and weather conditions. These worlds were created using the Unity game engine and a novel real-to-virtual cloning method. These photo-realistic synthetic videos are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels (cf. below for download links).

Citations for datasets included in this collection: @article{yan2024df40, title={DF40: Toward Next-Generation Deepfake Detection}, author={Yan, Zhiyuan and Yao, Taiping and Chen, Shen and Zhao, Yandan and Fu, Xinghe and Zhu, Junwei and Luo, Donghao and Yuan, Li and Wang, Chengjie and Ding, Shouhong and others}, journal={arXiv preprint arXiv:2406.13495}, year={2024} }

Global Artificial Intelligence (AI) Video Generator market size is expected to reach $1.92 billion by 2029 at 22.6%, segmented as by solution, ai video editing software, ai video creation tools, ai-based animation software

Outline

• This dataset is originally created for the Knowledge Graph Reasoning Challenge for Social Issues (KGRC4SI)
• Video data that simulates daily life actions in a virtual space from Scenario Data.
• Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).
• Knowledge Graph Embedding Data are created for reasoning based on machine learning
• This data is open to the public as open data

Details

• Videos

  • mp4 format
  • 203 action scenarios
  • For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos
  • Videos with slowly moving characters simulate the movements of elderly people.

• Knowledge Graphs

  • RDF format
  • 203 knowledge graphs corresponding to the videos
  • Includes schema and location supplement information
  • The schema is described below
  • SPARQL endpoints and query examples are available

• Script Data

  • txt format
  • Data provided to VirtualHome2KG to generate videos and knowledge graphs
  • Includes the action title and a brief description in text format.
• Embedding
  • Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (https://dglke.dgl.ai/doc/)
  • Embedding Vectors created with jRDF2vec (https://github.com/dwslab/jRDF2Vec).

Specification of Ontology

• Please refer to the specification for descriptions of all classes, instances, and properties: https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm

Related Resources

• KGRC4SI Final Presentations with automatic English subtitles (YouTube)
• VirtualHome2KG (Software)
• VirtualHome-AIST (Unity)
• VirtualHome-AIST (Python API)
• Visualization Tool (Software)
• Script Editor (Software)

Classify video clips with natural scenes of actions performed by people visible in the videos.

See the UCF101 Dataset web page: https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101

This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: https://doi.org/10.5281/zenodo.7882861 .

Based on this code: https://keras.io/examples/vision/video_classification/ (needs to be updated, if has not yet been already; see the issue: https://github.com/keras-team/keras-io/issues/1342).

Testing if data can be downloaded from figshare with `wget`, see: https://github.com/mojaveazure/angsd-wrapper/issues/10

For generating the subset, see this notebook: https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).

I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date.

Cite this dataset as:

Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402. https://doi.org/10.48550/arXiv.1212.0402

To download the dataset via the command line, please use:

wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz
tar xf ucf101_top5.tar.gz

SynPick is a synthetic dataset for dynamic scene understanding in bin-picking scenarios. In contrast to existing datasets, this dataset is both situated in a realistic industrial application domain -- inspired by the well-known Amazon Robotics Challenge (ARC) -- and features dynamic scenes with authentic picking actions as chosen by our picking heuristic developed for the ARC 2017. The dataset is compatible with the popular BOP dataset format.

The dataset consists of 21 Synthetic videos with 503,232 with diverse lightning and 3 different views of each video.

The Artificial Intelligence (AI) in Media and Entertainment market is experiencing rapid growth, driven by increasing adoption of AI-powered tools across various segments. The market, estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% during the forecast period (2025-2033). This robust growth is fueled by several key factors. Firstly, the rising demand for personalized content and efficient content creation is pushing media and entertainment companies to leverage AI for tasks like content recommendation, automated video editing, and personalized advertising. Secondly, advancements in AI technologies, including deep learning and natural language processing, are enabling more sophisticated applications like real-time content analysis, AI-driven scriptwriting, and synthetic media generation. This is further enhanced by decreasing computational costs and the increasing availability of large datasets for training AI models. Finally, the growing adoption of cloud-based AI solutions is simplifying accessibility and reducing the infrastructural burden for businesses of all sizes. However, the market also faces challenges. High initial investment costs associated with AI implementation can be a barrier for smaller companies. Data security and privacy concerns, particularly regarding the use of personal data for AI-driven personalization, remain a significant hurdle. Moreover, the ethical implications of AI-generated content, such as deepfakes and the potential for bias in algorithms, require careful consideration and robust regulatory frameworks. Despite these restraints, the overall market outlook for AI in media and entertainment remains exceptionally positive, promising substantial growth and transformation across the industry in the coming years. The continued development of more sophisticated and user-friendly AI tools, coupled with increasing industry awareness of the benefits, will undoubtedly drive further adoption and expansion of this exciting market.