NVIDIA at CVPR 2024

Seattle Convention Center
Seattle, WA

June 17–21

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    At the Computer Vision and Pattern Recognition (CVPR) conference, NVIDIA researchers shared their latest groundbreaking innovations—including fifty-seven papers. Explore the work to see how NVIDIA Research collaborates with CVPR members to deliver AI breakthroughs across the community.

    Image courtesy of Fraunhofer IML

    NVIDIA Announces Omniverse Microservices to Supercharge Physical AI

    NVIDIA Omniverse Cloud Sensor RTX generates synthetic data to speed up AI development of autonomous vehicles, robotic arms, mobile robots, humanoids and smart spaces.

    Read About Omniverse Cloud Sensor RTX

    NVIDIA Research Wins CVPR Autonomous Grand Challenge for End-to-End Driving

    NVIDIA Research utilized accelerated computing and generative AI innovations to  advance autonomous vehicle development and physical AI applications.

    Learn More About the Challenge

    NVIDIA Advances Physical AI at CVPR With the Largest Indoor Synthetic Dataset

    For the annual AI City Challenge, hundreds of teams from around the world tested their AI models on physically based datasets generated with NVIDIA Omniverse.

    Read About the AI City Challenge

    End-to-End Driving at Scale With Hydra-MDP

    Building an autonomous system to navigate in the complex physical world is extremely challenging. The system must perceive its environment and make quick, sensible decisions.

    Explore the Technology
    Supercharge Robotics Workflows Using Isaac Sim 4.0 and Isaac Lab

    Supercharge Robotics Workflows Using Isaac Sim 4.0 and Isaac Lab

    The latest releases of Isaac Sim 4.0, which includes NVIDIA PhysX 5.4 and Isaac Lab, and brings powerful new features and AI enhancements to robotics simulation.

    Read About NVIDIA Isaac Sim 4.0

    Image courtesy of Boston Dynamics

    Closing the Sim-to-Real Gap: Training Spot Quadruped Locomotion with Isaac Lab

    A collaboration between Boston Dynamics, NVIDIA, and The AI Institute seamlessly deploys quadrupeds from the virtual to the real world with NVIDIA Isaac Lab.

    Read About NVIDIA Isaac Lab

    Like No Place You’ve Ever Worked

    Working at NVIDIA, you’ll solve some of the world’s hardest problems and discover never-before-seen ways to improve the quality of life for people everywhere. From healthcare to robots, self-driving cars to blockbuster movies, you’ll experience it all. Plus, there’s a growing list of new opportunities every single day. Explore all of our open roles, including internships and new college graduate positions.

    Learn more about our current job openings, as well as university jobs.

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    NVIDIA Research Papers at CVPR 2024

    NVIDIA’s accepted papers at CVPR 2024 feature a range of groundbreaking research in the field of computer vision. From human motion forecasting to extracting triangular 3D models, materials, and lighting from images, explore the work NVIDIA is bringing to the CVPR community. 

    *Denotes equal contribution to the paper.

    Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

    Yijia Weng (Stanford University) · Bowen Wen (NVIDIA) · Jonathan Tremblay (NVIDIA) · Valts Blukis (NVIDIA) · Dieter Fox (University of Washington) · Leonidas Guibas (Stanford University) · Stan Birchfield (NVIDIA) | Paper

    Align Your Gaussians: Text-to-4D With Dynamic 3D Gaussians and Composed Diffusion Models

    Huan Ling (NVIDIA, University of Toronto) · Seung Wook Kim (NVIDIA) · Antonio Torralba (MIT) · Sanja Fidler (University of Toronto) · Karsten Kreis (NVIDIA) | Paper

    FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

    Bowen Wen (NVIDIA) · Wei Yang (NVIDIA) · Jan Kautz (NVIDIA) · Stan Birchfield (NVIDIA) | Paper

    Rendering Every Pixel for High-Fidelity Geometry in 3D GANs

    Alex Trevithick (None) · Matthew Chan (NVIDIA) · Towaki Takikawa (NVIDIA) · Umar Iqbal (None) · Shalini De Mello (NVIDIA Research) · Manmohan Chandraker (University of California, San Diego) · Ravi Ramamoorthi (None) · Koki Nagano (None) | Paper

    VILA: On Pretraining for Visual Language Models

    Ji Lin (MIT) · Danny Yin (NVIDIA) · Wei Ping (NVIDIA) · Pavlo Molchanov (NVIDIA) · Mohammad Shoeybi (NVIDIA) · Song Han (MIT) | Paper

    GAvatar: Animatable 3D Gaussian Avatars With Implicit Mesh Learning

    Ye Yuan (NVIDIA Research) · Xueting Li (NVIDIA) · Yangyi Huang (Zhejiang University) · Shalini De Mello (NVIDIA Research) · Koki Nagano (None) · Jan Kautz (NVIDIA) · Umar Iqbal (None) | Paper

    JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients

    Woo Kyoung Han (Korea University) · Sunghoon Im (Daegu Gyeongbuk Institute of Science and Technology) · Jaedeok Kim (NVIDIA) · Kyong Hwan Jin (Korea University) | Paper

    Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation

    Ji-Jia Wu (National Taiwan University) · Andy Chia-Hao Chang (National Yang Ming Chiao Tung University) · Chieh-Yu Chuang (National Yang Ming Chiao Tung University) · Chun-Pei Chen (National Yang Ming Chiao Tung University) · Yu-Lun Liu (National Yang Ming Chiao Tung University) · Min-Hung Chen (NVIDIA) · Hou-Ning Hu (MediaTek Inc.) · Yung-Yu Chuang (National Taiwan University) · Yen-Yu Lin (National Yang Ming Chiao Tung University) | Paper

    HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data

    Mengqi Zhang (University of California, San Diego) · Yang Fu (University of California, San Diego) · Zheng Ding (University of California, San Diego) · Sifei Liu (NVIDIA) · Zhuowen Tu (University of California, San Diego) · Xiaolong Wang (University of California, San Diego) | Paper

    Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation

    Razvan Pasca (None) · Alexey Gavryushin (ETH Zurich) · Muhammad Hamza (University of Zurich) · Yen-Ling Kuo (University of Virginia) · Kaichun Mo (NVIDIA Research) · Luc Van Gool (ETH Zurich, KU Leuven, Institute for Computer Science, AI, and Technology) · Otmar Hilliges (None) · Xi Wang (None) | Paper

    Snapshot Lidar: Fourier embedding of phasors for single-image depth reconstruction

    Sarah Friday (Dartmouth College) · Yunzi Shi (Dartmouth College) · Yaswanth Kumar Cherivirala (University of Michigan, NVIDIA) · Vishwanath Saragadam (University of California, Riverside) · Adithya Pediredla (Dartmouth College) | Poster

    Analyzing and Improving the Training Dynamics of Diffusion Models

    Tero Karras (NVIDIA) · Miika Aittala (NVIDIA) · Jaakko Lehtinen (Aalto University, NVIDIA) · Janne Hellsten (NVIDIA) · Timo Aila (NVIDIA) · Samuli Laine (NVIDIA) | Paper

    BodyMAP—Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed

    Abhishek Tandon (Carnegie Mellon University) · Anujraaj Goyal (Carnegie Mellon University) · Henry M. Clever (NVIDIA) · Zackory Erickson (Carnegie Mellon University) | Paper

    PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation

    Ardian Umam (National Yang Ming Chiao Tung University) · Cheng-Kun Yang (MediaTek) · Min-Hung Chen (NVIDIA) · Jen-Hui Chuang (None) · Yen-Yu Lin (National Yang Ming Chiao Tung University) | Paper

    What Moves Together Belongs Together

    Jenny Seidenschwarz (Technische Universität München) · Aljoša Ošep (Carnegie Mellon University) · Francesco Ferroni (NVIDIA) · Simon Lucey (University of Adelaide) · Laura Leal-Taixe (NVIDIA) | Paper

    UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes

    David Rozenberszki (None) · Or Litany (NVIDIA, Technion) · Angela Dai (Technical University of Munich) | Paper

    Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

    Xunjiang Gu (University of Toronto) · Guanyu Song (University of Toronto) · Igor Gilitschenski (University of Toronto) · Marco Pavone (NVIDIA) · Boris Ivanovic (NVIDIA) | Paper

    SatSynth: Augmenting Image-Mask Pairs Through Diffusion Models for Aerial Semantic Segmentation

    Aysim Toker (Technical University Munich) · Marvin Eisenberger (Technical University Munich) · Daniel Cremers (Technical University Munich) · Laura Leal-Taixe (NVIDIA) | Paper

    Category-Level Multi-Part Multi-Joint 3D Shape Assembly

    Yichen Li (MIT) · Kaichun Mo (NVIDIA Research) · Yueqi Duan (None) · He Wang (None) · Jiequan Zhang (None) · Lin Shao (National University of Singapore) · Wojciech Matusik (MIT) · Leonidas Guibas (Stanford University) | Paper

    Dynamic LiDAR Resimulation Using Compositional Neural Fields

    Hanfeng Wu (None) · Xingxing Zuo (Caltech) · Stefan Leutenegger (Technische Universität München) · Or Litany (NVIDIA, Technion) · Konrad Schindler (ETH Zurich) · Shengyu Huang (None) | Paper

    NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

    Nilesh Kulkarni (None) · Davis Rempe (NVIDIA) · Kyle Genova (Google) · Abhijit Kundu (Google) · Justin Johnson (University of Michigan) · David Fouhey (New York University) · Leonidas Guibas (Stanford University) | Paper

    Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer

    Rafail Fridman (Weizmann Institute of Science) · Danah Yatim (Weizmann Institute of Science) · Omer Bar-Tal (Weizmann Institute of Science) · Yoni Kasten (NVIDIA Research) · Tali Dekel (Weizmann Institute of Science) | Paper

    Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty From Pretrained Models

    Gianni Franchi (ENSTA Paris) · Olivier Laurent (Université Paris-Saclay) · Maxence Leguéry (ENSTA Paris) · Andrei Bursuc (valeo.ai) · Andrea Pilzer (NVIDIA) · Angela Yao (National University of Singapore) | Paper

    Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction

    Cheng Sun (NVIDIA) · Wei-En Tai (National Tsinghua University) · Yu-Lin Shih (National Tsinghua University) · Kuan-Wei Chen (National Tsinghua University) · Yong-Jing Syu (National Tsinghua University) · Kent Selwyn The (National Tsinghua University) · Yu-Chiang Frank Wang (NVIDIA) · Hwann-Tzong Chen (National Tsing Hua University) | Paper

    Outdoor Scene Extrapolation With Hierarchical Generative Cellular Automata

    Dongsu Zhang (Seoul National University) · Francis Williams (NVIDIA) · Žan Gojčič (NVIDIA) · Karsten Kreis (NVIDIA) · Sanja Fidler (University of Toronto) · Young Min Kim (Seoul National University) · Amlan Kar (NVIDIA) | Poster

    Condition-Aware Neural Network for Controlled Image Generation

    Han Cai (MIT) · Muyang Li (None) · Qinsheng Zhang (Georgia Institute of Technology) · Ming-Yu Liu (NVIDIA) · Song Han (MIT) | Paper

    PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

    Ruining Deng (Vanderbilt University) · Quan Liu (Vanderbilt University) · Can Cui (Vanderbilt University) · Tianyuan Yao (Vanderbilt University) · Jialin Yue (Vanderbilt University) · Juming Xiong (Vanderbilt University) · Lining yu (Vanderbilt University) · Yifei Wu (Vanderbilt University) · Mengmeng Yin (Vanderbilt University) · Yu Wang (Vanderbilt University Medical Center) · Shilin Zhao (Vanderbilt University) · Yucheng Tang (NVIDIA) · Haichun Yang (Vanderbilt University Medical School) · Yuankai Huo (Vanderbilt University) | Paper

    NeRFDeformer: NeRF Transformation From a Single View via 3D Scene Flows

    Zhenggang Tang (University of Illinois Urbana-Champaign) · Jason Ren (Apple) · Xiaoming Zhao (Univerity of Illinois Urbana-Champaign) · Bowen Wen (NVIDIA) · Jonathan Tremblay (NVIDIA) · Stan Birchfield (NVIDIA) · Alexander G. Schwing (University of Illinois Urbana-Champaign) | Poster

    PerAda: Parameter-Efficient Federated Learning Personalization With Generalization Guarantees

    Chulin Xie (University of Illinois Urbana-Champaign) · De-An Huang (NVIDIA) · Wenda Chu (Caltech) · Daguang Xu (NVIDIA) · Chaowei Xiao (Arizona State University) · Bo Li (University of Illinois Urbana-Champaign) · Anima Anandkumar (Caltech) | Paper

    Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression

    Hancheng Ye (Fudan University) · Chong Yu (Fudan University, NVIDIA) · Peng Ye (Fudan University) · Renqiu Xia (Shanghai Jiao Tong University) · Bo Zhang (Shanghai AI Laboratory) · Yansong Tang (Tsinghua University) · Jiwen Lu (Tsinghua University) · Tao Chen (Fudan University) | Paper

    Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation

    Hoang Chuong Nguyen (Australian National University) · Tianyu Wang (Australian National University) · Jose M. Alvarez (NVIDIA) · Miaomiao Liu (Australian National University) | Paper

    GSNeRF: Generalizable Semantic Neural Radiance Fields With Enhanced 3D Scene Understanding

    Zi-Ting Chou (National Taiwan University) · Sheng-Yu Huang (National Taiwan University) · I-Jieh Liu (National Taiwan University) · Yu-Chiang Frank Wang (NVIDIA) | Paper

    Breathing Life Into Sketches Using Text-to-Video Priors

    Rinon Gal (Tel Aviv University, NVIDIA) · Yael Vinker (Tel Aviv University) · Yuval Alaluf (Tel Aviv University) · Amit H. Bermano (Tel Aviv University, Technion) · Daniel Cohen-Or (Google) · Ariel Shamir (Reichman University) · Gal Chechik (NVIDIA, Bar-Ilan University) | Paper

    Self-correcting LLM-Controlled Diffusion

    Tsung-Han Wu (University of California, Berkeley) · Long Lian (University of California, Berkeley) · Joseph Gonzalez (University of California, Berkeley) · Boyi Li (University of California, Berkeley, NVIDIA) · Trevor Darrell (University of California, Berkeley) | Paper

    Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding

    Wujian Peng (Fudan University) · Sicheng Xie (Fudan University) · Zuyao You (Fudan University) · Shiyi Lan (NVIDIA) · Zuxuan Wu (Fudan University) | Paper

    Driving Everywhere With Large Language Model Policy Adaptation

    Boyi Li (University of California, Berkeley, NVIDIA) · Yue Wang (MIT) · Jiageng Mao (Chinese University of Hong Kong) · Boris Ivanovic (NVIDIA) · Sushant Veer (NVIDIA) · Karen Leung (University of Washington) · Marco Pavone (NVIDIA) | Paper

    RegionGPT: Towards Region Understanding Vision Language Model

    Qiushan Guo ( University of Hong Kong) · Shalini De Mello (NVIDIA Research) · Danny Yin (NVIDIA) · Wonmin Byeon (NVIDIA) · Ka Chun Cheung (NVIDIA) · Yizhou Yu ( University of Hong Kong) · Ping Luo ( University of Hong Kong) · Sifei Liu (NVIDIA) | Paper

    JeDi: Joint-Image Diffusion Models for Fine-Tuning-Free Personalized Text-to-Image Generation

    Yu Zeng (None) · Vishal M. Patel (Johns Hopkins University) · Haochen Wang (Toyota Technological Institute at Chicago) · Xun Huang (NVIDIA) · Ting-Chun Wang (NVIDIA) · Ming-Yu Liu (NVIDIA) · Yogesh Balaji (NVIDIA) | Poster

    CurveCloudNet: Processing Point Clouds With 1D Structure

    Colton Stearns (None) · Alex Fu (Illumix) · Jiateng Liu (University of Illinois Urbana-Champaign) · Jeong Joon Park (Stanford University) · Davis Rempe (NVIDIA) · Despoina Paschalidou (Stanford University) · Leonidas Guibas (Stanford University) | Paper

    MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes

    Bor Shiun Wang (None) · Chien-Yi Wang (NVIDIA) · Wei-Chen Chiu (None) | Paper Coming Soon

    A Unified Approach for Text- and Image-Guided 4D Scene Generation

    Yufeng Zheng (ETH Zurich, Max Planck Institute for Intelligent Systems) · Xueting Li (NVIDIA) · Koki Nagano (None) · Sifei Liu (NVIDIA) · Otmar Hilliges (None) · Shalini De Mello (NVIDIA Research) | Paper

    BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection

    Zhenxin Li (Fudan University) · Shiyi Lan (NVIDIA) · Jose M. Alvarez (NVIDIA) · Zuxuan Wu (Fudan University) | Paper

    3DiffTection: 3D Object Detection With Geometry-Aware Diffusion Features

    Chenfeng Xu (University of California, Berkeley) · Huan Ling (NVIDIA, University of Toronto) · Sanja Fidler (University of Toronto) · Or Litany (NVIDIA, Technion) | Paper

    XCube: Large-Scale 3D Generative Modeling Using Sparse Voxel Hierarchies

    Xuanchi Ren (University of Toronto) · Jiahui Huang (None) · Xiaohui Zeng (University of Toronto) · Ken Museth (NVIDIA) · Sanja Fidler (University of Toronto) · Francis Williams (NVIDIA) | Paper

    Degree-of-Freedom Matters: Inferring Dynamics From Point Trajectories

    Yan Zhang (ETH Zurich) · Sergey Prokudin (ETH Zurich) · Marko Mihajlovic (Swiss Federal Institute of Technology) · Qianli Ma (NVIDIA Research) · Siyu Tang (ETH Zurich) | Project Page

    AHIVE: Anatomy-Aware Hierarchical Vision Encoding for Interactive Radiology Report Retrieval

    Sixing Yan (None) · William K. Cheung (Hong Kong Baptist University) · Ivor Tsang (A*STAR) · Wan Hang Keith Chiu (Queen Elizabeth Hospital) · Tong Terence (The Chinese University of Hong Kong) · Ka Chun Cheung (NVIDIA) · Simon See (NVIDIA) | Poster

    AM-RADIO: Agglomerative Models—Reduce All Domains Into One

    Mike Ranzinger (NVIDIA Research) · Greg Heinrich (NVIDIA) · Jan Kautz (NVIDIA) · Pavlo Molchanov (NVIDIA) | Paper

    COLMAP-Free 3D Gaussian Splatting

    Yang Fu (University of California, San Diego) · Sifei Liu (NVIDIA) · Amey Kulkarni (NVIDIA) · Jan Kautz (NVIDIA) · Alexei A. Efros (University of California, Berkeley) · Xiaolong Wang (University of California, San Diego) | Paper

    Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation

    Yunhao Ge (NVIDIA) · Xiaohui Zeng (University of Toronto) · Jacob Huffman (NVIDIA) · Tsung-Yi Lin (NVIDIA) · Ming-Yu Liu (NVIDIA) · Yin Cui (NVIDIA) | Paper

    Addressing Background Context Bias in Few-Shot Segmentation Through Iterative Modulation

    Lanyun Zhu (Singapore University of Technology and Design) · Tianrun Chen (Zhejiang University) · Jianxiong Yin (NVIDIA) · Simon See (NVIDIA) · Jun Liu () | Poster

    PARA-Drive: Parallelized Architecture for Real-Time Autonomous Driving

    Xinshuo Weng (NVIDIA) · Boris Ivanovic (NVIDIA) · Yan Wang (NVIDIA) · Yue Wang (MIT) · Marco Pavone (NVIDIA) | Paper Coming Soon

    Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?

    Zhiqi Li (Nanjing University) · Zhiding Yu (NVIDIA) · Shiyi Lan (NVIDIA) · Jiahan Li (Nanjing University) · Jan Kautz (NVIDIA) · Tong Lu (Nanjing University) · Jose M. Alvarez (NVIDIA) | Paper

    Improving Distant 3D Object Detection Using 2D Box Supervision

    Zetong Yang (The Chinese University of Hong Kong) · Zhiding Yu (NVIDIA) · Christopher Choy (Stanford University) · Renhao Wang (University of California, Berkeley) · Anima Anandkumar (Caltech) · Jose M. Alvarez (NVIDIA) | Paper

    PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios

    Jingbo Wang (Shanghai AI Laboratory) · Zhengyi Luo (Carnegie Mellon University) · Ye Yuan (NVIDIA Research) · Yixuan LI (The Chinese University of Hong Kong) · Bo Dai (Shanghai AI Laboratory) | Paper Coming Soon

    RGBD Objects in the Wild: Scaling Real-World 3D Object Learning From RGB-D Videos

    Hongchi Xia (Shanghai Jiaotong University) · Yang Fu (University of California, San Diego) · Sifei Liu (NVIDIA) · Xiaolong Wang (University of California, San Diego) | Paper

    MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer

    Jianjian Cao (Fudan University) · Peng Ye (Fudan University) · Shengze Li (Fudan University) · Chong Yu (Fudan University, NVIDIA) · Yansong Tang (Tsinghua University) · Jiwen Lu (Tsinghua University) · Tao Chen (Fudan University) | Paper

    DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models

    Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Ming-Yu Liu, Kai Li, Song Han | Paper

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    Deep Dive

    Training Computer Vision Models With Synthetic Data

    Watch the Livestream Read the Blog Learn more about Synthetic Data Generation

    Create, Design, and Deploy Robotics Applications Using New NVIDIA Isaac Foundation Models and Workflows

    The application of robotics is rapidly expanding in diverse environments that require enhanced capabilities such as perception, mapping, navigation, load handling, object grasping, and intricate assembly tasks. By integrating NVIDIA AI acceleration, robots can tackle complex tasks with greater precision and efficiency, unlocking their full potential across diverse applications.

    Read the Blog

    Toronto AI Lab

    NVIDIA’s Toronto AI Research Group consists of a group of researchers passionate about the intersection of computer vision, machine learning, and computer graphics.

    Explore the Lab

    NVIDIA VILA

    VILA is a visual language model that brings visual information into large language models (LLMs). To leverage powerful LLMs, VILA uses a visual encoder to encode images or video as visual tokens and then inputs these tokens into the LLM as if they’re a foreign language.

    Read the Technical Deep Dive

    Featured Demos

    Explore how NVIDIA technologies are transforming a variety of industries with powerful demos that highlight the latest breakthroughs in AI, data science, graphics, healthcare, and more.

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    Transform Edge AI Applications With Generative AI

    A generative AI application is one of the reference applications offered with NVIDIA Metropolis microservices for NVIDIA Jetson™, a suite of cloud-native building blocks for developing edge AI applications and solutions.

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    Fusing Real Time AI With Digital Twins

    Discover the AI that'll drive the next phase of industrial automation—how it'll be developed, refined, and first deployed in simulation via digital twins.

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    NVIDIA Visual Insight Agent (VIA)

    This new generation of visual AI agents will help nearly every industry summarize, search, and extract actionable insights from video using natural language.

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    Introducing Generative Physical AI

    NVIDIA Omniverse™ is a development platform for virtual world simulation that combines real-time, physically based rendering, physics simulation, and generative AI technologies. In Omniverse, robots can learn to be robots—minimizing the sim-to-real gap and maximizing the transfer of learned behavior.

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    NVIDIA Research AI Playground

    Discover our most recent AI research and the new capabilities deep learning brings to visual and audio applications. Explore the latest innovations and see how you can bring them into your own work.

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    Resources

    Free Computer Vision Course

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    Meet the Trailblazing Startups at CVPR

    Explore some of the NVIDIA Inception members exhibiting their computer vision solutions at CVPR.

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