most citedCosmos World Foundation Model Platform for Physical AI

11 citations · 24 across the 15 of their papers we have counts for

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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

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency

Kaiwen Zheng, Yuji Wang, Qianli Ma +7

Although continuous-time consistency models (e.g., sCM, MeanFlow) are theoretically principled and empirically powerful for fast academic-scale diffusion, its applicability to larg…

cs.CV2025

Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light

Ali Hassani, Fengzhe Zhou, Aditya Kane +13

Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the l…

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★ 3 cited

Edify 3D: Scalable High-Quality 3D Asset Generation

NVIDIA, :, Maciej Bala +22

We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at mul…

cs.CV2024

Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models

NVIDIA, :, Yuval Atzmon +29

We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space dif…

cs.CV2024★ 3 cited

EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation

Jiaxiang Tang, Zhaoshuo Li, Zekun Hao +4

Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressiv…

cs.CV2024★ 1 cited

Condition-Aware Neural Network for Controlled Image Generation

Han Cai, Muyang Li, Zhuoyang Zhang +3

We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the imag…

cs.CV2024★ 2 cited

DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models

Muyang Li, Tianle Cai, Jiaxin Cao +7

Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the e…