Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908295438008320 |
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| author | NVIDIA : Alhaija, Hassan Abu Alvarez, Jose Bala, Maciej Cai, Tiffany Cao, Tianshi Cha, Liz Chen, Joshua Chen, Mike Ferroni, Francesco Fidler, Sanja Fox, Dieter Ge, Yunhao Gu, Jinwei Hassani, Ali Isaev, Michael Jannaty, Pooya Lan, Shiyi Lasser, Tobias Ling, Huan Liu, Ming-Yu Liu, Xian Lu, Yifan Luo, Alice Ma, Qianli Mao, Hanzi Ramos, Fabio Ren, Xuanchi Shen, Tianchang Sun, Xinglong Tang, Shitao Wang, Ting-Chun Wu, Jay Xu, Jiashu Xu, Stella Xie, Kevin Ye, Yuchong Yang, Xiaodong Zeng, Xiaohui Zeng, Yu |
| author_facet | NVIDIA : Alhaija, Hassan Abu Alvarez, Jose Bala, Maciej Cai, Tiffany Cao, Tianshi Cha, Liz Chen, Joshua Chen, Mike Ferroni, Francesco Fidler, Sanja Fox, Dieter Ge, Yunhao Gu, Jinwei Hassani, Ali Isaev, Michael Jannaty, Pooya Lan, Shiyi Lasser, Tobias Ling, Huan Liu, Ming-Yu Liu, Xian Lu, Yifan Luo, Alice Ma, Qianli Mao, Hanzi Ramos, Fabio Ren, Xuanchi Shen, Tianchang Sun, Xinglong Tang, Shitao Wang, Ting-Chun Wu, Jay Xu, Jiashu Xu, Stella Xie, Kevin Ye, Yuchong Yang, Xiaodong Zeng, Xiaohui Zeng, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control NVIDIA : Alhaija, Hassan Abu Alvarez, Jose Bala, Maciej Cai, Tiffany Cao, Tianshi Cha, Liz Chen, Joshua Chen, Mike Ferroni, Francesco Fidler, Sanja Fox, Dieter Ge, Yunhao Gu, Jinwei Hassani, Ali Isaev, Michael Jannaty, Pooya Lan, Shiyi Lasser, Tobias Ling, Huan Liu, Ming-Yu Liu, Xian Lu, Yifan Luo, Alice Ma, Qianli Mao, Hanzi Ramos, Fabio Ren, Xuanchi Shen, Tianchang Sun, Xinglong Tang, Shitao Wang, Ting-Chun Wu, Jay Xu, Jiashu Xu, Stella Xie, Kevin Ye, Yuchong Yang, Xiaodong Zeng, Xiaohui Zeng, Yu Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics 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. |
| title | Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2503.14492 |