Quick note on a few updates to the site software that have rolled out recently:All frontend packages updated to the latest where possibleBackend services updated to recent LTSMost notably, the editor has been updated to the latest version. The previous editor software was nearly 5 years old. I know a lot of users have had editor issues with newer browsers. Some of those have possibly been fixed.Various formatting changes to improve readability and layout

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In a highly anticipated fireside chat at SIGGRAPH 2024, NVIDIA founder and CEO Jensen Huang and Meta founder and CEO Mark Zuckerberg discussed the transformative potential of open source AI and AI assistants.Zuckerberg kicked off the discussion by announcing the launch of AI Studio, a new platform that allows users to create, share and discover AI characters, making AI more accessible to millions of creators and small businesses.Every single restaurant, every single website will probably, in the future, have these AIs Huang said.just like every business has an email address and a website and a social media account, I think, in the future, every business is going to have an AI, Zuckerberg responded.Zuckerberg has gotten it right before. Huang credited Zuckerberg and Meta with being leaders in AI, even if only some have noticed until recently.You guys have done amazing AI work, Huang said, citing advancements from Meta in computer vision, language models, real-time translation. We all use Pytorch, that comes out of Meta.The Importance of Open Source in Advancing AIZuckerberg highlighted the importance of open source in advancing AI with the two business leaders emphasizing the importance of open platforms for innovation. Meta has rapidly emerged as a leader in AI, putting it to work throughout its businesses most notably with Meta AI, which is used across Facebook, Instagram and WhatsApp and advancing open-source AI throughout the industry, most recently with the release of Llama 3.1.The open-source model represents a significant investment of time and training resources. The largest version of Llama boasts 405 billion parameters and was trained on over 16,000 NVIDIA H100 GPUs.One of the things that drives quality improvements is it used to be that you have a different model for each type of content, Zuckerberg explained.A the models get bigger and more general, that gets better and better. So, I kind of dream of one day like you can almost imagine all of Facebook or Instagram being like a single AI model that has unified all these different content types and systems together, he added.Zuckerberg sees collaboration as key to more advancements. In a blog post released last week, Zuckerberg wrote that the release of Llama 3.1 promises to be an inflection point in adopting open source in AI.These advancements promise more tools to foster engagement, create compelling and personalized content such as digital avatars and build virtual worlds.More broadly, the advancement of AI across a broad ecosystem promises to supercharge human productivity, for example, by giving every human on earth a digital assistant or assistants allowing people to live richer lives that they can interact with quickly and fluidly.I feel like Im collaborating with WhatsApp, Huang said. Imagine Im sitting here typing, and its generating the images as Im going. I go back and change my words, and its generating other images.Vision for the FutureLooking ahead, both CEOs shared their visions for the future.Zuckerberg expressed optimism about bringing AI together with the real world through eyeglasses nothing his companys collaboration with eyewear maker Luxotic that can be used to help transform education, entertainment and work.Huang emphasized how interacting with AIs is becoming more fluid, moving beyond just text-based interactions.Todays AI is kind of turn-based. You say something, it says something back to you, Huang said. In the future, AI could contemplate multiple options, or come up with a tree of options and simulate outcomes, making it much more powerful.Throughout the conversation, the two leaders playfully bantered about everything from fashion to steak sandwiches, ending the discussion by exchanging leather jackets.Zuckerberg give Huang with a black leather shearling jacket with an enormous hood.Huang gave Zuckerberg his own leather jacket, which he got from his wife, Lori, just for SIGGRAPH, quipping that it was just two hours old.Well this ones yours, Zuckerberg said with a smile. This is worth more because its used.

The generative AI revolution with deep roots in visual computing is amplifying human creativity even as accelerated computing promises significant gains in energy efficiency, NVIDIA founder and CEO Jensen Huang said Monday.That makes this weeks SIGGRAPH professional graphics conference, in Denver, the logical venue to discuss whats next.Everybody will have an AI assistant, Huang said. Every single company, every single job within the company, will have AI assistance.But even as generative AI promises to amplify human productivity, Huang said the accelerated computing technology that underpins it promises to make computing more energy efficient.Accelerated computing helps you save so much energy, 20 times, 50 times, and doing the same processing, Huang said. The first thing we have to do, as a society, is accelerate every application we can: this reduces the amount of energy being used all over the world.The conversation follows a spate of announcements from NVIDIA today.NVIDIA introduced a new suite of NIM microservices tailored for diverse workflows, including OpenUSD, 3D modeling, physics, materials, robotics, industrial digital twins and physical AI.These advancements aim to enhance developer capabilities, particularly with the integration of Hugging Face Inference-as-a-Service on DGX Cloud.In addition, Shutterstock has launched a Generative 3D Service, while Getty Images has upgraded its offerings using NVIDIA Edify technology.In the realm of AI and graphics, NVIDIA has revealed new OpenUSD NIM microservices and reference workflows designed for generative physical AI applications.This includes a program for accelerating humanoid robotics development through new NIM microservices for robotics simulation and more.Finally, WPP, the worlds largest advertising agency, is using Omniverse-driven generative AI for The Coca-Cola Company, helping drive brand authenticity, showcasing the practical applications of NVIDIAs advancements in AI technology across various industries.Huang and Goode started their conversation by exploring how visual computing gave rise to everything from computer games to digital animation to GPU-accelerated computing and, most recently, generative AI powered by industrial-scale AI factories.All these advancements build on one another. Robotics, for example, requires advanced AI and photorealistic virtual worlds where AI can be trained before being deployed into next-generation humanoid robots.Huang explained that robotics requires three computers: one to train the AI, one to test the AI in a physically accurate simulation, and one within the robot itself.Just about every industry is going to be affected by this, whether its scientific computing trying to do a better job predicting the weather with a lot less energy, to augmenting and collaborating with creators to generate images, or generating virtual scenes for industrial visualization, Huang said. Robotic self-driving cars are all going to be transformed by generative AI.Likewise, NVIDIA Omniverse systems built around the OpenUSD standard will also be key to harnessing generative AI to create assets that the worlds largest brands can use.By pulling from brand assets that live in Omniverse, which can capture brand assets, these systems can capture and replicate carefully curated brand magic.Finally, all these systems visual computing, simulation and large-language models will come together to create digital humans who can help people interact with digital systems of all kinds.One of the things that were announcing here this week is the concept of digital agents, digital AIs that will augment every single job in the company, Huang said.And so one of the most important use cases that people are discovering is customer service, Huang said. In the future, my guess is that its going to be human still, but AI in the loop.All of this, like any new tool, promises to amplify human productivity and creativity. Imagine the stories that youre going to be able to tell with these tools, Huang said.

When The Coca-Cola Company produces thirst-quenching marketing, the creative elements of campaigns arent just left to chance theres a recipe for the magic. Now, the beverage company, through its partnership with WPP Open X, is beginning to scale its global campaigns with generative AI from NVIDIA Omniverse and NVIDIA NIM microservices.With NVIDIA, we can personalize and customize Coke and meals imagery across 100-plus markets, delivering on hyperlocal relevance with speed and at global scale, said Samir Bhutada, global vice president of StudioX Digital Transformation at The Coca-Cola Company.Coca-Cola has been working with WPP to develop digital twin tools and roll out Prod X a custom production studio experience created specifically for the beverage maker to use globally.WPP announced today at SIGGRAPH that The Coca-Cola Company will be an early adopter for integrating the new NVIDIA NIM microservices for Universal Scene Description (aka OpenUSD) into its Prod X roadmap. OpenUSD is a 3D framework that enables interoperability between software tools and data types for building virtual worlds. NIM inference microservices provide models as optimized containers.The USD Search NIM allows WPP to tap into a large archive of models to create on-brand assets, and the USD Code NIM can be used to assemble them into scenes.These NIM microservices will enable Prod X users to create 3D advertising assets that contain culturally relevant elements on a global scale, using prompt engineering to quickly make adjustments to AI-generated images so that brands can better target their products at local markets.Tapping Into NVIDIA NIM Microservices to Deploy Generative AIWPP said that the NVIDIA NIM microservices will have a lasting impact on the 3D engineering and art world.The USD Search NIM can make WPPs massive visual asset libraries quickly available via written prompts. The USD Code NIM allows developers to enter prompts and get Python code to create novel 3D worlds.The beauty of the solution is that it compresses multiple phases of the production process into a single interface and process, said Perry Nightingale, senior vice president of creative AI at WPP, of the new NIM microservices. It empowers artists to get more out of the technology and create better work.Redefining Content Production With Production StudioWPP recently announced the release of Production Studio on WPP Open, the companys intelligent marketing operating system powered by AI. Co-developed with its production company, Hogarth, Production Studio taps into the Omniverse development platform and OpenUSD for its generative AI-enabled product configurator workflows.Production Studio can streamline and automate multilingual text, image and video creation, simplifying content creation for advertisers and marketers, and directly addresses the challenges advertisers continue to face in producing brand-compliant and product-accurate content at scale.Our groundbreaking research with NVIDIA Omniverse for the past few years, and the research and development associated with having built our own core USD pipeline and decades of experience in 3D workflows, is what made it possible for us to stand up a tailored experience like this for The Coca-Cola Company, said Priti Mhatre, managing director for strategic consulting and AI at Hogarth.SIGGRAPH attendees can hear more about WPPs efforts by joining the companys session on Robotics, Generative AI, and OpenUSD: How WPP Is Building the Future of Creativity.NVIDIA founder and CEO Jensen Huang will also be featured at the event in fireside chats with Meta founder and CEO Mark Zuckerberg and WIRED Senior Writer Lauren Goode. Watch the talks and other sessions from NVIDIA at SIGGRAPH 2024 on demand.Photo credit: WPP, The Coca-Cola CompanySee notice regarding software product information.

NVIDIA announced at SIGGRAPH fVDB, a new deep-learning framework for generating AI-ready virtual representations of the real world.fVDB is built on top of OpenVDB, the industry-standard library for simulating and rendering sparse volumetric data such as water, fire, smoke and clouds.Generative physical AI, such as autonomous vehicles and robots that inhabit the real world, need to have spatial intelligence the ability to understand and operate in 3D space.Capturing the large scale and super-fine details of the world around us is essential. But converting reality into a virtual representation to train AI is hard.Raw data for real-world environments can be collected through many different techniques, like neural radiance fields (NeRFs) and lidar. fVDB translates this data into massive, AI-ready environments rendered in real time.Building on a decade of innovation in the OpenVDB standard, the introduction of fVDB at SIGGRAPH represents a significant leap forward in how industries can benefit from digital twins of the real world.Reality-scale virtual environments are used for training autonomous agents. City-scale 3D models are captured by drones for climate science and disaster planning. Today, 3D generative AI is even used to plan urban spaces and smart cities.fVDB enables industries to tap into spatial intelligence on a larger scale and with higher resolution than ever before, making physical AI even smarter.The framework builds NVIDIA-accelerated AI operators on top of NanoVDB, a GPU-accelerated data structure for efficient 3D simulations. These operators include convolution, pooling, attention and meshing, all of which are designed for high-performance 3D deep learning applications.AI operators allow businesses to build complex neural networks for spatial intelligence, like large-scale point cloud reconstruction and 3D generative modeling.fVDB is the result of a long-running effort by NVIDIAs research team and is already used to support NVIDIA Research, NVIDIA DRIVE and NVIDIA Omniverse projects that require high-fidelity models of large, complex real-world spaces.Key Advantages of fVDBLarger: 4x larger spatial scale than prior frameworksFaster: 3.5x faster than prior frameworksInteroperable: Businesses can fully tap into massive real-world datasets. fVDB reads VDB datasets into full-sized 3D environments. AI-ready and real-time rendered for building physical AI with spatial intelligence.More powerful: 10x more operators than prior frameworks. fVDB simplifies processes by combining functionalities that previously required multiple deep-learning libraries.fVDB will soon be available as NVIDIA NIM inference microservices. A trio of the microservices will enable businesses to incorporate fVDB into OpenUSD workflows, generating AI-ready OpenUSD geometry in NVIDIA Omniverse, a development platform for industrial digitalization and generative physical AI applications. They are:fVDB Mesh Generation NIM Generates digital 3D environments of the real worldfVDB NeRF-XL NIM Generates large-scale NeRFs in OpenUSD using Omniverse Cloud APIsfVDB Physics Super-Res NIM Performs super-resolution to generate an OpenUSD-based, high-resolution physics simulationOver the past decade, OpenVDB, housed at the Academy Software Foundation, has earned multiple Academy Awards as a core technology used throughout the visual-effects industry. It has since grown beyond entertainment to industrial and scientific uses, like industrial design and robotics.NVIDIA continues to enhance the open-source OpenVDB library. Four years ago, the company introduced NanoVDB, which added GPU support to OpenVDB. This delivered an order-of-magnitude speed-up, enabling faster performance and easier development, and opening the door to real-time simulation and rendering.Two years ago, NVIDIA introduced NeuralVDB, which builds machine learning on top of NanoVDB to compress the memory footprint of VDB volumes up to 100x, allowing creators, developers and researchers to interact with extremely large and complex datasets.fVDB builds AI operators on top of NanoVDB to unlock spatial intelligence at the scale of reality. Apply to the early-access program for the fVDB PyTorch extension. fVDB will also be available as part of the OpenVDB GitHub repository.Dive deeper into fVDB in this technical blog and watch how accelerated computing and generative AI are transforming industries and creating new opportunities for innovation and growth in NVIDIA founder and CEO Jensen Huangs two fireside chats at SIGGRAPH.See notice regarding software product information.

The worlds brands and agencies are using generative AI to create advertising and marketing content, but it doesnt always provide the desired outputs.NVIDIA offers a comprehensive set of technologies bringing together generative AI, NVIDIA NIM microservices, NVIDIA Omniverse and Universal Scene Description (OpenUSD) to allow developers to build applications and workflows that enable brand-accurate, targeted and efficient advertising at scale.Developers can use the USD Search NIM microservice to provide artists access to a vast archive of OpenUSD-based, brand-approved assets such as products, props and environments and when integrated with the USD Code NIM microservice, assembly of these scenes can be accelerated. Teams can also use the NVIDIA Edify-powered Shutterstock Generative 3D service to rapidly generate 3D new assets using AI.The scenes, once constructed, can be rendered to a 2D image and used as input to direct an AI-powered image generator to create precise, brand-accurate visuals.Global agencies, developers and production studios are tapping these technologies to revolutionize every aspect of the advertising process, from creative production and content supply chain to dynamic creative optimization.WPP announced at SIGGRAPH its adoption of the technologies, naming The Coca-Cola Company the first brand to embrace generative AI with Omniverse and NVIDIA NIM microservices.Agencies and Service Providers Increase Adoption of OmniverseThe NVIDIA Omniverse development platform has seen widespread adoption for its ability to build accurate digital twins of products. These virtual replicas allow brands and agencies to create ultra-photorealistic and physically accurate 3D product configurators, helping to increase personalization, customer engagement and loyalty, and average selling prices, and reducing return rates.Digital twins can also serve many purposes and be updated to meet shifting consumer preferences with minimal time, cost and effort, helping flexibly scale content production.Agencies and Service Providers Increase Adoption of OmniverseThe NVIDIA Omniverse development platform has seen widespread adoption for its ability to build accurate digital twins of products. These virtual replicas allow brands and agencies to create ultra-photorealistic and physically accurate 3D product configurators, helping to increase personalization, customer engagement and loyalty, and average selling prices, and reducing return rates.Digital twins can also serve many purposes and be updated to meet shifting consumer preferences with minimal time, cost and effort, helping flexibly scale content production.Image courtesy of Monks, Hatch.Global marketing and technology services company Monks developed Monks.Flow, an AI-centric professional managed service that uses the Omniverse platform to help brands virtually explore different customizable product designs and unlock scale and hyper-personalization across any customer journey.NVIDIA Omniverse and OpenUSDs interoperability accelerates connectivity between marketing, technology and product development, said Lewis Smithingham, executive vice president of strategic industries at Monks. Combining Omniverse with Monks streamlined marketing and technology services, we infuse AI throughout the product development pipeline and help accelerate technological and creative possibilities for clients.Collective World, a creative and technology company, is an early adopter of real-time 3D, OpenUSD and NVIDIA Omniverse, using them to create high-quality digital campaigns for customers like Unilever and EE. The technologies allow Collective to develop digital twins, delivering consistent, high-quality product content at scale to streamline advertising and marketing campaigns.Building on its use of NVIDIA technologies, Collective World announced at SIGGRAPH that it has joined the NVIDIA Partner Network.Product digital twin configurator and content generation tool built by Collective on NVIDIA Omniverse.INDG is using Omniverse to introduce new capabilities into Grip, its popular software tool. Grip uses OpenUSD and generative AI to streamline and enhance the creation process, delivering stunning, high-fidelity marketing content faster than ever.This integration helps bring significant efficiencies to every brand by delivering seamless interoperability and enabling real-time visualization, said Frans Vriendsendorp, CEO of INDG. Harnessing the potential of USD to eliminate the lock-in to proprietary formats, the combination of Grip and Omniverse is helping set new standards in the realm of digital content creation.Image generated with Grip, copyright BeiersdorfTo get started building applications and services using OpenUSD, Omniverse and NVIDIA AI, check out the product configurator developer resources and the generative AI workflow for content creation reference architecture, or submit a contact form to learn more or connect with NVIDIAs ecosystem of service providers.Watch NVIDIA founder and CEO Jensen Huangs fireside chats, as well as other on-demand sessions from NVIDIA at SIGGRAPH.Stay up to date by subscribing to our newsletter, and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X.

One of the worlds largest AI communities comprising 4 million developers on the Hugging Face platform is gaining easy access to NVIDIA-accelerated inference on some of the most popular AI models.New inference-as-a-service capabilities will enable developers to rapidly deploy leading large language models such as the Llama 3 family and Mistral AI models with optimization from NVIDIA NIM microservices running on NVIDIA DGX Cloud.Announced today at the SIGGRAPH conference, the service will help developers quickly prototype with open-source AI models hosted on the Hugging Face Hub and deploy them in production. Enterprise Hub users can tap serverless inference for increased flexibility, minimal infrastructure overhead and optimized performance with NVIDIA NIM.The inference service complements Train on DGX Cloud, an AI training service already available on Hugging Face.Developers facing a growing number of open-source models can benefit from a hub where they can easily compare options. These training and inference tools give Hugging Face developers new ways to experiment with, test and deploy cutting-edge models on NVIDIA-accelerated infrastructure. Theyre made easily accessible using the Train and Deploy drop-down menus on Hugging Face model cards, letting users get started with just a few clicks.Get started with inference-as-a-service powered by NVIDIA NIM.Beyond a Token Gesture NVIDIA NIM Brings Big BenefitsNVIDIA NIM is a collection of AI microservices including NVIDIA AI foundation models and open-source community models optimized for inference using industry-standard application programming interfaces, or APIs.NIM offers users higher efficiency in processing tokens the units of data used and generated by a language model. The optimized microservices also improve the efficiency of the underlying NVIDIA DGX Cloud infrastructure, which can increase the speed of critical AI applications.This means developers see faster, more robust results from an AI model accessed as a NIM compared with other versions of the model. The 70-billion-parameter version of Llama 3, for example, delivers up to 5x higher throughput when accessed as a NIM compared with off-the-shelf deployment on NVIDIA H100 Tensor Core GPU-powered systems.Near-Instant Access to DGX Cloud Provides Accessible AI AccelerationThe NVIDIA DGX Cloud platform is purpose-built for generative AI, offering developers easy access to reliable accelerated computing infrastructure that can help them bring production-ready applications to market faster.The platform provides scalable GPU resources that support every step of AI development, from prototype to production, without requiring developers to make long-term AI infrastructure commitments.Hugging Face inference-as-a-service on NVIDIA DGX Cloud powered by NIM microservices offers easy access to compute resources that are optimized for AI deployment, enabling users to experiment with the latest AI models in an enterprise-grade environment.More on NVIDIA NIM at SIGGRAPHAt SIGGRAPH, NVIDIA also introduced generative AI models and NIM microservices for the OpenUSD framework to accelerate developers abilities to build highly accurate virtual worlds for the next evolution of AI.To experience more than 100 NVIDIA NIM microservices with applications across industries, visit ai.nvidia.com.

Generative AI is unlocking new ways for enterprises to engage customers through digital human avatars.At SIGGRAPH, NVIDIA previewed James, an interactive digital human that can connect with people using emotions, humor and more. James is based on a customer-service workflow using NVIDIA ACE, a reference design for creating custom, hyperrealistic, interactive avatars. Users will soon be able to talk with James in real time at ai.nvidia.com.NVIDIA also showcased at the computer graphics conference the latest advancements to the NVIDIA Maxine AI platform, including Maxine 3D and Audio2Face-2D for an immersive telepresence experience.Developers can use Maxine and NVIDIA ACE digital human technologies to make customer interactions with digital interfaces more engaging and natural. ACE technologies enable digital human development with AI models for speech and translation, vision, intelligence, lifelike animation and behavior, and realistic appearance.Companies across industries are using Maxine and ACE to deliver immersive virtual customer experiences.Meet James, a Digital Brand Ambassadorhttps://blogs.nvidia.com/wp-content/uploads/2024/07/AceCut-2.mp4Built on top of NVIDIA NIM microservices, James is a virtual assistant that can provide contextually accurate responses.Using retrieval-augmented generation (RAG), James can accurately tell users about the latest NVIDIA technologies. ACE allows developers to use their own data to create domain-specific avatars that can communicate relevant information to customers.James is powered by the latest NVIDIA RTX rendering technologies for advanced, lifelike animations. His natural-sounding voice is powered by ElevenLabs. NVIDIA ACE lets developers customize animation, voice and language when building avatars tailored for different use cases.NVIDIA Maxine Enhances Digital Humans in TelepresenceMaxine, a platform for deploying cutting-edge AI features that enhance the audio and video quality of digital humans, enables the use of real-time, photorealistic 2D and 3D avatars with video-conferencing devices.Maxine 3D converts 2D video portrait inputs into 3D avatars, allowing the integration of highly realistic digital humans in video conferencing and other two-way communication applications. The technology will soon be available in early access.Audio2Face-2D, currently in early access, animates static portraits based on audio input, creating dynamic, speaking digital humans from a single image. Try the technology at ai.nvidia.com.Companies Embracing Digital Human ApplicationsHTC, Looking Glass, Reply and UneeQ are among the latest companies using NVIDIA ACE and Maxine across a broad range of use cases, including customer service agents, and telepresence experiences in entertainment, retail and hospitality.At SIGGRAPH, digital human technology developer UneeQ is showcasing two new demos.The first spotlights cloud-rendered digital humans powered by NVIDIA GPUs with local, in-browser computer vision for enhanced scalability and privacy, and animated using the Audio2Face-3D NVIDIA NIM microservice. UneeQs Synapse technology processes anonymized user data and feeds it to a large language model (LLM) for more accurate, responsive interactions.The second demo runs on a single NVIDIA RTX GPU-powered laptop, featuring an advanced digital human powered by Gemma 7B LLM, RAG and the NVIDIA Audio2Face-3D NIM microservice.Both demos showcase UneeQs NVIDIA-powered efforts to develop digital humans that can react to users facial expressions and actions, pushing the boundaries of realism in virtual customer service experiences.HTC Viverse has integrated the Audio2Face-3D NVIDIA NIM microservice into its VIVERSE AI agent for dynamic facial animation and lip sync, allowing for more natural and immersive user interactions.Hologram technology company Looking Glass Magic Mirror demo at SIGGRAPH uses a simple camera setup and Maxines advanced 3D AI capabilities to generate a real-time holographic feed of users faces on its newly launched, group-viewable Looking Glass 16-inch and 32-inch Spatial Displays.Reply is unveiling an enhanced version of Futura, its cutting-edge digital human developed for Costa Crocieres Costa Smeralda cruise ship. Powered by Audio2Face-3D NVIDIA NIM and Riva ASR NIM microservices, Futuras speech-synthesis capabilities tap advanced technologies including GPT-4o, LlamaIndex for RAG and Microsoft Azure text-to-speech services.Futura also incorporates Replys proprietary affective computing technology, alongside Hume AI and MorphCast, for comprehensive emotion recognition. Built using Unreal Engine 5.4.3 and MetaHuman Creator with NVIDIA ACE-powered facial animation, Futura supports six languages. The intelligent assistant can help plan personalized port visits, suggest tailored itineraries and facilitate tour bookings.In addition, Futura refines recommendations based on guest feedback and uses a specially created knowledge base to provide informative city presentations, enhancing tourist itineraries. Futura aims to enhance customer service and offer immersive interactions in real-world scenarios, leading to streamlined operations and driving business growth.Learn more about NVIDIA ACE and NVIDIA Maxine.Discover how accelerated computing and generative AI are transforming industries and creating new opportunities for innovation by watching NVIDIA founder and CEO Jensen Huangs fireside chats at SIGGRAPH.See notice regarding software product information.

Millions of people already use generative AI to assist in writing and learning. Now, the technology can also help them more effectively navigate the physical world.NVIDIA announced at SIGGRAPH generative physical AI advancements including the NVIDIA Metropolis reference workflow for building interactive visual AI agents and new NVIDIA NIM microservices that will help developers train physical machines and improve how they handle complex tasks.These include three fVDB NIM microservices that support NVIDIAs new deep learning framework for 3D worlds, as well as the USD Code, USD Search and USD Validate NIM microservices for working with Universal Scene Description (aka OpenUSD).The NVIDIA OpenUSD NIM microservices work together with the worlds first generative AI models for OpenUSD development also developed by NVIDIA to enable developers to incorporate generative AI copilots and agents into USD workflows and broaden the possibilities of 3D worlds.NVIDIA NIM Microservices Transform Physical AI LandscapesPhysical AI uses advanced simulations and learning methods to help robots and other industrial automation more effectively perceive, reason and navigate their surroundings. The technology is transforming industries like manufacturing and healthcare, and advancing smart spaces with robots, factory and warehouse technologies, surgical AI agents and cars that can operate more autonomously and precisely.NVIDIA offers a broad range of NIM microservices customized for specific models and industry domains. NVIDIAs suite of NIM microservices tailored for physical AI supports capabilities for speech and translation, vision and intelligence, and realistic animation and behavior.Turning Visual AI Agents Into Visionaries With NVIDIA NIMVisual AI agents use computer vision capabilities to perceive and interact with the physical world and perform reasoning tasks.Highly perceptive and interactive visual AI agents are powered by a new class of generative AI models called vision language models (VLMs), which bridge digital perception and real-world interaction in physical AI workloads to enable enhanced decision-making, accuracy, interactivity and performance. With VLMs, developers can build vision AI agents that can more effectively handle challenging tasks, even in complex environments.Generative AI-powered visual AI agents are rapidly being deployed across hospitals, factories, warehouses, retail stores, airports, traffic intersections and more.To help physical AI developers more easily build high-performing, custom visual AI agents, NVIDIA offers NIM microservices and reference workflows for physical AI. The NVIDIA Metropolis reference workflow provides a simple, structured approach for customizing, building and deploying visual AI agents, as detailed in the blog.NVIDIA NIM Helps K2K Make Palermo More Efficient, Safe and SecureCity traffic managers in Palermo, Italy, deployed visual AI agents using NVIDIA NIM to uncover physical insights that help them better manage roadways.K2K, an NVIDIA Metropolis partner, is leading the effort, integrating NVIDIA NIM microservices and VLMs into AI agents that analyze the citys live traffic cameras in real time. City officials can ask the agents questions in natural language and receive fast, accurate insights on street activity and suggestions on how to improve the citys operations, like adjusting traffic light timing.Leading global electronics giants Foxconn and Pegatron have adopted physical AI, NIM microservices and Metropolis reference workflows to more efficiently design and run their massive manufacturing operations.The companies are building virtual factories in simulation to save significant time and costs. Theyre also running more thorough tests and refinements for their physical AI including AI multi-camera and visual AI agents in digital twins before real-world deployment, improving worker safety and leading to operational efficiencies.Bridging the Simulation-to-Reality Gap With Synthetic Data GenerationMany AI-driven businesses are now adopting a simulation-first approach for generative physical AI projects involving real-world industrial automation.Manufacturing, factory logistics and robotics companies need to manage intricate human-worker interactions, advanced facilities and expensive equipment. NVIDIA physical AI software, tools and platforms including physical AI and VLM NIM microservices, reference workflows and fVDB can help them streamline the highly complex engineering required to create digital representations or virtual environments that accurately mimic real-world conditions.VLMs are seeing widespread adoption across industries because of their ability to generate highly realistic imagery. However, these models can be challenging to train because of the immense volume of data required to create an accurate physical AI model.Synthetic data generated from digital twins using computer simulations offers a powerful alternative to real-world datasets, which can be expensive and sometimes impossible to acquire for model training, depending on the use case.Tools like NVIDIA NIM microservices and Omniverse Replicator let developers build generative AI-enabled synthetic data pipelines to accelerate the creation of robust, diverse datasets for training physical AI. This enhances the adaptability and performance of models such as VLMs, enabling them to generalize more effectively across industries and use cases.AvailabilityDevelopers can access state-of-the-art, open and NVIDIA-built foundation AI models and NIM microservices at ai.nvidia.com. The Metropolis NIM reference workflow is available in the GitHub repository, and Metropolis VIA microservices are available for download in developer preview.OpenUSD NIM microservices are available in preview through the NVIDIA API catalog.Watch how accelerated computing and generative AI are transforming industries and creating new opportunities for innovation and growth in NVIDIA founder and CEO Jensen Huangs fireside chats at SIGGRAPH.See notice regarding software product information.