The Next Generation of AI-Driven Robots and Autonomous Vehicles: The Importance of High-Fidelity, Physics-Aware Training Data
Cosmos Transfer for Photorealistic Videos Grounded in Physics
Cosmos Transfer WFM generates high-fidelity world scenes from structural inputs, ensuring precise spatial alignment and scene composition. Employing the ControlNet architecture, Cosmos Transfer preserves pre-trained knowledge, enabling structured, consistent outputs. It utilizes spatiotemporal control maps to dynamically align synthetic and real-world representations, enabling fine-grained control over scene composition, object placement, and motion dynamics.
Key Capabilities:
- Generate scalable, photorealistic synthetic data that aligns with real-world physics.
- Control object interactions and scene composition through structured multimodal inputs.
Using Cosmos Transfer for Controllable Synthetic Data
With generative AI APIs and SDKs, NVIDIA Omniverse accelerates physical AI simulation. Developers use NVIDIA Omniverse, built on OpenUSD, to create 3D scenes that accurately simulate real-world environments for training and testing robots and autonomous vehicles. These simulations serve as ground truth video inputs for Cosmos Transfer, combined with annotations and text instructions. Cosmos Transfer enhances photorealism while varying environment, lighting, and visual conditions to generate scalable, diverse world states.
Cosmos Predict for Scalable, Photorealistic Synthetic Data
Cosmos Predict WFM provides a strong foundation for training downstream world models in robotics and autonomous vehicles. You can post-train these models to generate actions instead of video for policy modeling or adapt it for visual-language understanding to create custom perception AI models.
Cosmos Reason to Perceive, Reason, and Respond Intelligently
Cosmos Reason is a fully customizable multimodal AI reasoning model that is purpose-built to understand motion, object interactions, and space-time relationships. Using chain-of-thought (CoT) reasoning, the model interprets visual input, predicts outcomes based on the given prompt, and rewards the optimal decision. Unlike text-based LLMs, it grounds reasoning in real-world physics, generating clear, context-aware responses in natural language.
Training Pipeline:
- Pretraining: Uses a Vision Transformer (ViT) to process video frames into structured embeddings, aligning them with text for a shared understanding of objects, actions, and spatial relationships.
- Supervised fine-tuning (SFT): Specializes the model in physical reasoning across two key levels. General fine-tuning enhances language grounding and multimodal perception using diverse video-text datasets, while more training on physical AI data sharpens the model’s ability to reason about real-world interactions.
Conclusion:
The next generation of AI-driven robots and autonomous vehicles relies on high-fidelity, physics-aware training data. Cosmos WFMs, such as Cosmos Transfer and Cosmos Predict, accelerate the creation of scalable, photorealistic synthetic data and controllable world states, enabling effective generalization from simulation to real-world deployment. Cosmos Reason, a fully customizable multimodal AI reasoning model, grounds reasoning in real-world physics, generating clear, context-aware responses in natural language.
Frequently Asked Questions:
Q: What is the purpose of Cosmos WFMs?
A: To accelerate the creation of high-fidelity, physics-aware training data for AI-driven robots and autonomous vehicles.
Q: What is Cosmos Transfer?
A: Cosmos Transfer generates high-fidelity world scenes from structural inputs, ensuring precise spatial alignment and scene composition.
Q: What is Cosmos Predict?
A: Cosmos Predict provides a strong foundation for training downstream world models in robotics and autonomous vehicles.
Q: What is Cosmos Reason?
A: Cosmos Reason is a fully customizable multimodal AI reasoning model that grounds reasoning in real-world physics, generating clear, context-aware responses in natural language.
Q: How do I get started with Cosmos WFMs?
A: Try Cosmos Predict preview NIM on build.nvidia.com. Use this workflow guide to use Cosmos Transfer for synthetic data generation. Explore free NVIDIA GTC 2025 Cosmos sessions.
