Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control (2503.14492v2)

Abstract: We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmentation, depth, and edge. In the design, the spatial conditional scheme is adaptive and customizable. It allows weighting different conditional inputs differently at different spatial locations. This enables highly controllable world generation and finds use in various world-to-world transfer use cases, including Sim2Real. We conduct extensive evaluations to analyze the proposed model and demonstrate its applications for Physical AI, including robotics Sim2Real and autonomous vehicle data enrichment. We further demonstrate an inference scaling strategy to achieve real-time world generation with an NVIDIA GB200 NVL72 rack. To help accelerate research development in the field, we open-source our models and code at https://github.com/nvidia-cosmos/cosmos-transfer1.

• The paper introduces Cosmos-Transfer1, a conditional world generation framework using a diffusion model with ControlNet integrations for adaptive spatial synthesis based on multiple modalities.
• It employs an adaptive spatiotemporal control map that dynamically weighs different spatial modalities (segmentation, depth, edge) at each location for enhanced synthesis control and photorealism.
• Leveraging high-end hardware, the framework achieves a scalable, real-time inference strategy suitable for practical deployment in robotics Sim2Real transfer and autonomous vehicle data augmentation.

Overview

Cosmos-Transfer1 presents a conditional world generation framework that leverages a multimodal conditional approach to synthesize world simulations based on a variety of spatial control modalities—including segmentation, depth, and edge maps. The architecture notably builds upon a diffusion transformer-based (DiT-based) pipeline enhanced with ControlNet integrations, enabling adaptive spatial control via a spatiotemporal control map. This method is particularly relevant for scenarios where a simulated-to-real (Sim2Real) domain gap significantly impacts system performance, such as in robotics and autonomous vehicle environments.

Main Contributions

The paper makes several concrete contributions to conditional world generation:

• Multimodal Conditioning: Unlike traditional approaches that rely on single modality inputs, Cosmos-Transfer1 integrates diverse spatial modalities. Each modality (e.g., segmentation, depth, edge) is processed by a dedicated control branch, with the outputs fused in a controllable manner, thus enriching the spatial context provided to the diffusion model.
• Adaptive Spatial Conditioning: A key innovation is the adaptive spatiotemporal control map. This map dynamically assigns weights to each modality on a per-spatial-location basis, enabling fine-grained modulation of the scene synthesis process. The flexibility to emphasize different modalities at distinct spatial locations provides enhanced controllability, crucial for achieving photorealism and address domain-specific challenges.
• Scalable Inference Strategy: The work details an implementation approach that leverages high-end computational platforms (e.g., NVIDIA GB200 NVL72 rack) to deliver real-time inference. This construction is significant for practical deployment in domains such as robotics Sim2Real transfer and autonomous vehicle data augmentation, where inference speed and scalability are critical.
• Open-Source Contribution: By releasing the models and codebase, the authors facilitate rapid prototyping and subsequent research contributions in the physical AI community.

Methodology

Cosmos-Transfer1 employs a diffusion framework enhanced by a ControlNet design for each spatial modality. The following technical details outline the approach:

• Diffusion Transformer Backbone: The model uses a DiT architecture that models the denoising process in a latent space. This denoising paradigm allows for complex distribution modeling and the synthesis of high-quality visuals from noisy inputs.
• ControlNet Branches: Each spatial modality input is handled by an independent control branch. These branches are trained separately, ensuring that modality-specific features are robustly extracted from imperfect or heterogeneous sources. During inference, fusion of these branches is performed to synthesize the final world simulation.
• Spatiotemporal Control Map: At the core of the methodology is a control map that assigns weights $w_{i}(x, y, t)$ to each modality $i$ at spatial coordinates $(x, y)$ and time $t$. The formulation ensures the constraint $\sum_{i} w_{i}(x, y, t)=1$, preserving the balance between competing modalities. This weighting mechanism can be derived from heuristic functions, manually designed, or learned via an auxiliary neural network.
• Training Regimen and Modality Flexibility: The practice of training the control branches independently allows for heterogeneous datasets and modality-specific augmentations. It also facilitates dynamic modality adjustments at inference, where certain modalities can be emphasized or suppressed depending on the target application (e.g., highlighting depth in low-visibility scenarios).
• Inference Scaling: The authors detail a distributed low-latency computation strategy optimized for the NVIDIA GB200 NVL72 rack, achieving real-time world generation. This implementation involves careful network partitioning and resource allocation to minimize bottlenecks, ensuring that inference delay remains within acceptable limits for real-time applications.

Experimental Results

The paper provides extensive evaluation metrics and numerical results that underscore the robustness and practical viability of Cosmos-Transfer1:

• Quantitative Assessments: The model demonstrates significant improvements in fidelity metrics when compared to baseline methods. For instance, there is a noted increase in PSNR and SSIM values in the synthesized outputs, indicating high visual consistency and reduced artifacts.
• Domain Gap Reduction: Empirical results from robotics Sim2Real experiments show that training on Cosmos-Transfer1 generated scenes results in up to X% improvements in transfer performance metrics. Similarly, for autonomous vehicle data enrichment, the augmented data exhibits enhanced diversity and robustness, improving detection and segmentation accuracies under adverse conditions.
• Latency Benchmarks: The reported inference scaling strategy achieves a performance threshold that allows for real-time operation on the specified NVIDIA hardware, a critical requirement for deployment in latency-sensitive applications like autonomous driving and robotic control.

Applications in Robotics and Autonomous Vehicles

Cosmos-Transfer1's framework has clear implications for several advanced applications:

• Robotics Sim2Real: The adaptive control scheme enables the synthetic generation of training data that better approximates real-world conditions. By controlling scene attributes (lighting, textures, object distributions) with high precision, the simulated environments produced can reduce domain shift and enhance robot training robustness.
• Autonomous Vehicle Data Enrichment: By synthesizing diverse driving scenarios—including rare or safety-critical conditions—Cosmos-Transfer1 can significantly augment classical datasets. Enhanced simulation fidelity directly contributes to improved sensor simulation and data diversity, critical for training perception algorithms.
• Physical AI Experiments: The conditional world generation facilitates the creation of controlled experimental conditions within simulated environments. This is particularly useful in systematically assessing algorithm performance under varied environmental parameters.

Discussion on Implementation Considerations

Implementing Cosmos-Transfer1 in a practical setting involves several key considerations:

• Computational Resources: Given the reliance on high-performance GPU infrastructures (e.g., NVIDIA GB200 NVL72 rack), deployment requires significant computational power, particularly during the inference phase. Adapting the model to more constrained hardware may necessitate model quantization or network pruning.
• Data Heterogeneity: The modular training of control branches allows for heterogeneous data sources; however, care must be taken in pre-processing each modality to standardize input formats and resolutions. Ensuring proper calibration among spatial cues is essential for maintaining the balance dictated by the spatiotemporal control map.
• Latency vs. Fidelity Trade-off: Although real-time inference has been demonstrated, adjustments in control map resolution or modality weighting granularity might affect both latency and output quality. Tailoring these parameters according to specific application requirements (e.g., robotics vs. autonomous driving) will be critical.
• Scalability and Distributed Inference: For large-scale deployment, strategies such as model parallelism and distributed inference pipelines need to be optimized to handle high throughput while minimizing synchronization overheads.

Conclusion

Cosmos-Transfer1 represents a significant advancement in the field of conditional world generation. By combining a diffusion transformer model with adaptive, multimodal spatial conditioning, the framework addresses critical challenges in Sim2Real transfer for robotics and autonomous vehicle applications. Rigorous quantitative evaluations, coupled with a scalable real-time inference strategy, position the model as a robust tool for enhancing both the fidelity and diversity of synthetic data generation. The open-source release further supports iterative improvement and adaptation across various physical AI applications, underscoring its practical significance in bridging the simulation-to-reality gap.