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20242026
most citedCosmos World Foundation Model Platform for Physical AI

11 citations · 19 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Plenoptic Video Generation

Xiao Fu, Shitao Tang, Min Shi +5

Camera-controlled generative video re-rendering methods, such as ReCamMaster, have achieved remarkable progress. However, despite their success in single-view setting, these works…

cs.CV2025

World Simulation with Video Foundation Models for Physical AI

NVIDIA, :, Arslan Ali +87

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 Text2…

cs.CV2025

ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary

Zeqi Gu, Yin Cui, Zhaoshuo Li +6

Designing 3D scenes is traditionally a challenging task that demands both artistic expertise and proficiency with complex software. Recent advances in text-to-3D generation have gr…

cs.CV2025★ 2 cited

Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control

NVIDIA, :, Hassan Abu Alhaija +38

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 segmen…

cs.CV2025★ 11 cited

Cosmos World Foundation Model Platform for Physical AI

NVIDIA, :, Niket Agarwal +76

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 th…

cs.CV2024★ 4 cited

Matting by Generation

Zhixiang Wang, Baiang Li, Jian Wang +4

This paper introduces an innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge. Our method harnesses the cap…