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NVIDIA
NVIDIA
CWIP (Contrastive World-Image Pre-training)
Model
NVIDIA
NVIDIA
CWIP (Contrastive World-Image Pre-training)

CWIP (Contrastive World-Image Pre-training) projects a camera frame and a world-model frame into a joint embedding space to score camera-to-world consistency and emit per-patch object-presence and object-type classifications.

This model is backed by NVIDIA's Plus Plus (++) Promise
to learn more about the quality of the datasets used to train this model.
FieldResponse
Intended Task/Domain:Synthetic Data Quality Evaluation
Model Type:Transformer
Intended Users:Developers and researchers building, evaluating, or auditing generative world foundation models for autonomous driving, including users of NVIDIA Cosmos and the open-source Cosmos Evaluator suite. Specific applications include per-frame embeddings, alignment scoring, per-patch defect detection, object-type classification, and automated quality scoring of driving-video generations.
Output:L2-normalized 1024-dimensional global embedding vectors for world-model frame and camera frame, and segmentation masks for per-patch defect classifications (MATCH, EXTRA, MISSING, WRONG, DONT_CARE) and object-type classifications over an 18-class taxonomy respectively.
Describe how the model works:CWIP projects a camera frame and a world-model frame into a joint embedding space to score camera-to-world consistency and emit per-patch object-presence and object-type classifications. It uses a transformer-based architecture to process visual inputs and generate embeddings and segmentation masks for quality scoring of driving-video generations.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:Not Applicable
Technical Limitations and Mitigation:This model may not detect all inconsistencies between world-model control frame and camera frame. This model is trained on pairs of world-model control frame and camera frame. For other control signals, adaptation to the world-model control format or re-training with those specific control signals may be needed.
Verified to have met prescribed NVIDIA quality standards:Yes
Performance Metrics:Precision, Recall, F1, and IoU
Potential Known Risks:This model may output inaccurate semantic segmentation or suboptimal global embeddings that lead to incorrect synthetic image/video defect detection.
Terms of Use/Licensing:OpenMDW-1.1