3 citations · 8 across the 12 of their papers we have counts for
12 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
DuoGen: Towards General Purpose Interleaved Multimodal Generation
Min Shi, Xiaohui Zeng, Jiannan Huang +13
Interleaved multimodal generation enables capabilities beyond unimodal generation models, such as step-by-step instructional guides, visual planning, and generating visual drafts f…
Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
Junjie Bai, Yu-Wei Chao, Qizhi Chen +13
The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday…
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA, :, Yan Wang +41
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…
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…
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…
Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
NVIDIA, :, Alisson Azzolini +51
Physical AI systems need to perceive, understand, and perform complex actions in the physical world. In this paper, we present the Cosmos-Reason1 models that can understand the phy…
Not-So-Optimal Transport Flows for 3D Point Cloud Generation
Ka-Hei Hui, Chao Liu, Xiaohui Zeng +2
Learning generative models of 3D point clouds is one of the fundamental problems in 3D generative learning. One of the key properties of point clouds is their permutation invarianc…
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…
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…
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
Jiahe Liu, Youran Qu, Qi Yan +3
Significant advancements have been made in video generative models recently. Unlike image generation, video generation presents greater challenges, requiring not only generating hi…
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation
Yunhao Ge, Xiaohui Zeng, Jacob Samuel Huffman +3
Existing automatic captioning methods for visual content face challenges such as lack of detail, content hallucination, and poor instruction following. In this work, we propose Vis…