World Simulation with Video Foundation Models for Physical AI
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World
Foundation Models for Physical AI. Built on a flow-based architecture,
[Cosmos-Predict2.5] unifies Text2World, Image2World, and Video2World generation
in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language
model, to provide richer text grounding and finer control of world simulation.
Trained on 200M curated video clips and refined with reinforcement
learning-based post-training, [Cosmos-Predict2.5] achieves substantial
improvements over [Cosmos-Predict1] in video quality and instruction alignment,
with models released at 2B and 14B scales. These capabilities enable more
reliable synthetic data generation, policy evaluation, and closed-loop
simulation for robotics and autonomous systems. We further extend the family
with [Cosmos-Transfer2.5], a control-net style framework for Sim2Real and
Real2Real world translation. Despite being 3.5times smaller than
[Cosmos-Transfer1], it delivers higher fidelity and robust long-horizon video
generation. Together, these advances establish [Cosmos-Predict2.5] and
[Cosmos-Transfer2.5] as versatile tools for scaling embodied intelligence. To
accelerate research and deployment in Physical AI, we release source code,
pretrained checkpoints, and curated benchmarks under the NVIDIA Open Model
License at https://github.com/nvidia-cosmos/cosmos-predict2.5 and
https://github.com/nvidia-cosmos/cosmos-transfer2.5. We hope these open
resources lower the barrier to adoption and foster innovation in building the
next generation of embodied intelligence.
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NVIDIA
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Arslan Ali
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Junjie Bai
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Maciej Bala
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Tianshi Cao
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Elizabeth Cha
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Yu-Wei Chao
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2025-11-24T07:59:25.953Z
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Yu-Wei Chao
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Prithvijit Chattopadhyay
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Mike Chen
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Yongxin Chen
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Yu Chen
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Shuai Cheng
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Yin Cui
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Yin Cui
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Jenna Diamond
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Linxi Fan
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Liang Feng
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Xiao Fu
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Jacob Huffman
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Pooya Jannaty
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Jan Kautz
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Grace Lam
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Xuan Li
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Zhaoshuo Li
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Maosheng Liao
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Chen-Hsuan Lin
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Huan Ling
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Ming-Yu Liu
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Xian Liu
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Yifan Lu
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Alice Luo
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Qianli Ma
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Hanzi Mao
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2025-11-04T20:24:41.033Z
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Kaichun Mo
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Seungjun Nah
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Haoxiang Wang
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Heng Wang
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Fangyin Wei
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Jiashu Xu
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Dinghao Yang
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Xiaodong Yang
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Haotian Ye
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Seonghyeon Ye
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Xiaohui Zeng
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Jing Zhang
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Qinsheng Zhang
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Kaiwen Zheng
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World Simulation with Video Foundation Models for Physical AI
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World
Foundation Models for Physical AI. Built on a flow-based architecture,
[Cosmos-Predict2.5] unifies Text2World, Image2World, and Video2World generation
in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language
model, to provide richer text grounding and finer control of world simulation.
Trained on 200M curated video clips and refined with reinforcement
learning-based post-training, [Cosmos-Predict2.5] achieves substantial
improvements over [Cosmos-Predict1] in video quality and instruction alignment,
with models released at 2B and 14B scales. These capabilities enable more
reliable synthetic data generation, policy evaluation, and closed-loop
simulation for robotics and autonomous systems. We further extend the family
with [Cosmos-Transfer2.5], a control-net style framework for Sim2Real and
Real2Real world translation. Despite being 3.5times smaller than
[Cosmos-Transfer1], it delivers higher fidelity and robust long-horizon video
generation. Together, these advances establish [Cosmos-Predict2.5] and
[Cosmos-Transfer2.5] as versatile tools for scaling embodied intelligence. To
accelerate research and deployment in Physical AI, we release source code,
pretrained checkpoints, and curated benchmarks under the NVIDIA Open Model
License at https://github.com/nvidia-cosmos/cosmos-predict2.5 and
https://github.com/nvidia-cosmos/cosmos-transfer2.5. We hope these open
resources lower the barrier to adoption and foster innovation in building the
next generation of embodied intelligence.
Cosmos-Predict2.5 and Cosmos-Transfer2.5 are advanced Physical AI models that unify text, image, and video generation, improve video quality and instruction alignment, and enable Sim2Real and Real2Real world translation with higher fidelity.
flow-based architecture Text2World Image2World Video2World Physical AI vision-language model reinforcement learning synthetic data generation policy evaluation closed-loop simulation robotics autonomous systems control-net Sim2Real Real2Real video generation embodied intelligence