Jinwei Gu

Niket Agarwal, Arslan Ali, Maciej Bala et al. · nvidia

Physical AI needs to be trained digitally first. It needs a digital twin of itself, the policy model, and a digital twin of the world, the world model. In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups. We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications. Our platform covers a video curation pipeline, pre-trained world foundation models, examples of post-training of pre-trained world foundation models, and video tokenizers. To help Physical AI builders solve the most critical problems of our society, we make Cosmos open-source and our models open-weight with permissive licenses available via https://github.com/nvidia-cosmos/cosmos-predict1.

Lingen Li, Mingde Yao, Xingyu Meng et al.

Modern end-to-end image signal processors (ISPs) can learn complex mappings from RAW/XYZ data to sRGB (and vice versa), opening new possibilities in image processing. However, the growing diversity of camera models, particularly in mobile devices, renders the development of individual ISPs unsustainable due to their limited versatility and adaptability across varied camera systems. In this paper, we introduce Uni-ISP, a novel pipeline that unifies ISP learning for diverse mobile cameras, delivering a highly accurate and adaptable processor. The core of Uni-ISP is leveraging device-aware embeddings through learning forward/inverse ISPs and its special training scheme. By doing so, Uni-ISP not only improves the performance of forward and inverse ISPs but also unlocks new applications previously inaccessible to conventional learned ISPs. To support this work, we construct a real-world 4K dataset, FiveCam, comprising more than 2,400 pairs of sRGB-RAW images captured synchronously by five smartphone cameras. Extensive experiments validate Uni-ISP's accuracy in learning forward and inverse ISPs (with improvements of +2.4dB/1.5dB PSNR), versatility in enabling new applications, and adaptability to new camera models.