project
Cosmos - NVIDIA's generative world modeling platform
Cosmos is NVIDIA's generative world modeling platform, accelerating the development of physical artificial intelligence (AI) systems, particularly in autonomous driving and robotics. Cosmos can accept prompts from text, images, or videos...
What is Cosmos?
Cosmos is NVIDIA's generative world modeling platform that accelerates the development of physical artificial intelligence (AI) systems, particularly in the fields of autonomous driving and robotics. Cosmos can accept cues from text, images, or video to generate highly realistic virtual world states, providing unique video outputs for autonomous driving and robotics applications. The platform integrates generative world models, advanced taggers, and accelerated video processing pipelines, helping developers generate large amounts of physically based synthetic data and reducing reliance on real-world data. Cosmos also provides security mechanisms to ensure data security and compliance. Developers can fine-tune Cosmos models to create customized AI models that meet specific application needs.
Main functions of Cosmos
- Generate virtual world stateCosmos can generate highly realistic virtual world states based on text, images, or video prompts, making it suitable for autonomous driving and robotics applications.
- Generative modelsThe platform uses generative models to quickly generate data similar to real-world scenarios, helping developers train and evaluate existing AI models.
- Advanced taggers and data processingCosmos integrates advanced labelers and an accelerated video processing pipeline, allowing the generated data to play a greater role in subsequent model training.
- Safety and ComplianceThe platform also provides security protection mechanisms to ensure data security and compliance.
- Open model licenseCosmos will be available in the Hugging Face and NVIDIA NGC catalogs as an open model license, allowing developers to create customized applications.
Cosmos's technical principles
- Generative World Foundation Model (WFM)Cosmos uses advanced generative modeling techniques, including diffusion models and autoregressive Transformer models, to generate synthetic data that is highly similar to real-world scenes.
- Advanced tokenizer (Cosmos Tokenizer)This tagger uses a sophisticated encoder-decoder architecture, combining 3D causal convolution and attention mechanisms to efficiently process spatiotemporal information. It can decompose images and videos into high-compression, high-quality tags, providing more efficient visual data for AI models.
- Accelerating the video processing pipeline (NeMo Curator)Cosmos integrates an accelerated video processing pipeline that can process large amounts of video data in a short time. For example, the NeMo Curator can process 20 million hours of video data in 14 days.
Cosmos Model Series
Nano model
- FeaturesSuitable for low-latency and real-time applications.
- Parameter sizeApproximately 4 billion parameters.
- Application scenariosSuitable for applications requiring rapid response, such as real-time video analytics and simple robot control tasks.
Super Model
- FeaturesProvides high-performance benchmarks.
- Parameter sizeApproximately 7 billion parameters.
- Application scenariosSuitable for applications requiring high performance and accuracy, such as environmental perception and decision support for autonomous vehicles, as well as simulation and training of complex robotic tasks.
Ultra model
- FeaturesPursuing the highest quality and accuracy.
- Parameter sizeApproximately 14 billion parameters.
- Application scenariosSuitable for applications with extremely high precision and quality requirements, such as high-precision autonomous driving simulation and complex industrial robot operation simulation.
Cosmos project address
- Project official website:https://research.nvidia.com/publication/2025-01_cosmos
- Github repository:https://github.com/NVIDIA/Cosmos
- HuggingFace model library:https://huggingface.co/collections/nvidia/cosmos
- Technical Papers:https://d1qx31qr3h6wln.cloudfront.net/publications/NVIDIA%20Cosmos
Application scenarios of Cosmos
- Driving Environment SimulationCosmos can generate synthetic data under various weather and road conditions, providing rich scenarios for the training of autonomous driving systems.
- Strategy Model OptimizationBy generating a large number of realistic driving scenarios, Cosmos can help autonomous driving systems perform reinforcement learning in simulated environments, optimize decision-making strategy models, and test performance under different scenarios.
- Complex Environment Adaptability TrainingCosmos can provide robots with real-time simulations of complex environments, enabling their perception systems to be trained using synthetic data.
- Navigation and Task ExecutionBased on the virtual world state generated by Cosmos, robots can better understand and adapt to their surroundings, achieving more precise navigation and task execution.
- Realistic scene generationCosmos can generate highly realistic virtual world states, suitable for virtual reality games and simulation training. For example, developers can use Omniverse to create 3D scenes and then use Cosmos to convert them into realistic scenes, allowing robots to train in a simulated environment.
- Industrial digital twinBy combining NVIDIA's Omniverse and Cosmos, industrial digital twin environments can be created for the simulation, testing, and optimization of factories and warehouses. This enables better manual design, operation, and optimization in complex production facilities and distribution center networks.
Cosmos Application Cases
- Uber's self-driving car developmentAs one of the first companies to adopt Cosmos, Uber has accelerated the development of a safe and scalable autonomous driving solution based on generative AI capabilities. Cosmos provides Uber's autonomous driving system with a wealth of synthetic data, helping it to train and optimize models in different driving scenarios, thereby improving the safety and reliability of its autonomous driving technology.
- XPeng Motors simulation trainingXPeng Motors also uses the Cosmos platform to simulate and train its autonomous driving algorithms by generating synthetic driving data under various weather and road conditions. For example, in simulated adverse weather conditions such as rain, snow, and fog, as well as different road conditions such as urban roads and highways, the autonomous driving system can learn how to better perceive the environment, make decisions, and execute operations, thereby improving the algorithm's performance in real street scenes.
- 1X Robot Dynamic Programming1X utilizes Cosmos' simulation engine to provide robots with high-fidelity mechanical, kinematic, and dynamic interaction modeling capabilities. Through closed-loop simulation, 1X robots can perform dynamic planning and environmental adaptation optimization in a virtual environment, achieving more accurate navigation and task execution in real-world applications.