NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehicle (AV) simulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoring (GTRS) method, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion Models (Read more in this blog.) FoundationStereo: Zero-Shot Stereo Matching (Best Paper nominee) Zero-Shot Monocular Scene Flow Estimation in the Wild (Best Paper nominee) Difix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models (Best Paper nominee) 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.

Editor’s note: This blog is a part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. Simulated driving environments enable engineers to safely and efficiently train, test and validate autonomous vehicles (AVs) across countless real-world and edge-case scenarios without the risks and costs of physical testing. These simulated environments can be created through neural reconstruction of real-world data from AV fleets or generated with world foundation models (WFMs) — neural networks that understand physics and real-world properties. WFMs can be used to generate synthetic datasets for enhanced AV simulation. To help physical AI developers build such simulated environments, NVIDIA unveiled major advances in WFMs at the GTC Paris and CVPR conferences earlier this month. These new capabilities enhance NVIDIA Cosmos — a platform of generative WFMs, advanced tokenizers, guardrails and accelerated data processing tools. Key innovations like Cosmos Predict-2, the Cosmos Transfer-1 NVIDIA preview NIM microservice and Cosmos Reason are improving how AV developers generate synthetic data, build realistic simulated environments and validate safety systems at unprecedented scale. Universal Scene Description (OpenUSD), a unified data framework and standard for physical AI applications, enables seamless integration and interoperability of simulation assets across the development pipeline. OpenUSD standardization plays a critical role in ensuring 3D pipelines are built to scale. NVIDIA Omniverse, a platform of application programming interfaces, software development kits and services for building OpenUSD-based physical AI applications, enables simulations from WFMs and neural reconstruction at world scale. Leading AV organizations — including Foretellix, Mcity, Oxa, Parallel Domain, Plus AI and Uber — are among the first to adopt Cosmos models. Foundations for Scalable, Realistic Simulation Cosmos Predict-2, NVIDIA’s latest WFM, generates high-quality synthetic data by predicting future world states from multimodal inputs like text, images and video. This capability is critical for creating temporally consistent, realistic scenarios that accelerate training and validation of AVs and robots. In addition, Cosmos Transfer, a control model that adds variations in weather, lighting and terrain to existing scenarios, will soon be available to 150,000 developers on CARLA, a leading open-source AV simulator. This greatly expands the broad AV developer community’s access to advanced AI-powered simulation tools. Developers can start integrating synthetic data into their own pipelines using the NVIDIA Physical AI Dataset. The latest release includes 40,000 clips generated using Cosmos. Building on these foundations, the Omniverse Blueprint for AV simulation provides a standardized, API-driven workflow for constructing rich digital twins, replaying real-world sensor data and generating new ground-truth data for closed-loop testing. The blueprint taps into OpenUSD’s layer-stacking and composition arcs, which enable developers to collaborate asynchronously and modify scenes nondestructively. This helps create modular, reusable scenario variants to efficiently generate different weather conditions, traffic patterns and edge cases. Driving the Future of AV Safety To bolster the operational safety of AV systems, NVIDIA earlier this year introduced NVIDIA Halos — a comprehensive safety platform that integrates the company’s full automotive hardware and software stack with AI research focused on AV safety. The new Cosmos models — Cosmos Predict- 2, Cosmos Transfer- 1 NIM and Cosmos Reason — deliver further safety enhancements to the Halos platform, enabling developers to create diverse, controllable and realistic scenarios for training and validating AV systems. These models, trained on massive multimodal datasets including driving data, amplify the breadth and depth of simulation, allowing for robust scenario coverage — including rare and safety-critical events — while supporting post-training customization for specialized AV tasks. At CVPR, NVIDIA was recognized as an Autonomous Grand Challenge winner, highlighting its leadership in advancing end-to-end AV workflows. The challenge used OpenUSD’s robust metadata and interoperability to simulate sensor inputs and vehicle trajectories in semi-reactive environments, achieving state-of-the-art results in safety and compliance. Learn more about how developers are leveraging tools like CARLA, Cosmos, and Omniverse to advance AV simulation in this livestream replay: Hear NVIDIA Director of Autonomous Vehicle Research Marco Pavone on the NVIDIA AI Podcast share how digital twins and high-fidelity simulation are improving vehicle testing, accelerating development and reducing real-world risks. Get Plugged Into the World of OpenUSD Learn more about what’s next for AV simulation with OpenUSD by watching the replay of NVIDIA founder and CEO Jensen Huang’s GTC Paris keynote. Looking for more live opportunities to learn more about OpenUSD? Don’t miss sessions and labs happening at SIGGRAPH 2025, August 10–14. Discover why developers and 3D practitioners are using OpenUSD and learn how to optimize 3D workflows with the self-paced “Learn OpenUSD” curriculum for 3D developers and practitioners, available for free through the NVIDIA Deep Learning Institute. Explore the Alliance for OpenUSD forum and the AOUSD website. Stay up to date by subscribing to NVIDIA Omniverse news, joining the community and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X.

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Inside Apple’s high-gloss standoff with AI ambition and the uncanny choreography of WWDC 2025There was a time when watching an Apple keynote — like Steve Jobs introducing the iPhone in 2007, the masterclass of all masterclasses in product launching — felt like watching a tightrope act. There was suspense. Live demos happened — sometimes they failed, and when they didn’t, the applause was real, not piped through a Dolby mix.These days, that tension is gone. Since 2020, in the wake of the pandemic, Apple events have become pre-recorded masterworks: drone shots sweeping over Apple Park, transitions smoother than a Pixar short, and executives delivering their lines like odd, IRL spatial personas. They move like human renderings: poised, confident, and just robotic enough to raise a brow. The kind of people who, if encountered in real life, would probably light up half a dozen red flags before a handshake is even offered. A case in point: the official “Liquid Glass” UI demo — it’s visually stunning, yes, but also uncanny, like a concept reel that forgot it needed to ship.https://medium.com/media/fcb3b16cc42621ba32153aff80ea1805/hrefAnd that’s the paradox. Not only has Apple trimmed down the content of WWDC, it’s also polished the delivery into something almost inhumanly controlled. Every keynote beat feels engineered to avoid risk, reduce friction, and glide past doubt. But in doing so, something vital slips away: the tension, the spontaneity, the sense that the future is being made, not just performed.Just one year earlier, WWDC 2024 opened with a cinematic cold open “somewhere over California”:https://medium.com/media/f97f45387353363264d99c341d4571b0/hrefPhil Schiller piloting an Apple-branded plane, iPod in hand, muttering “I’m getting too old for this stuff.” A perfect mix of Lethal Weapon camp and a winking message that yes, Classic-Apple was still at the controls — literally — flying its senior leadership straight toward Cupertino. Out the hatch, like high-altitude paratroopers of optimism, leapt the entire exec team, with Craig Federighi, always the go-to for Apple’s auto-ironic set pieces, leading the charge, donning a helmet literally resembling his own legendary mane. It was peak-bold, bizarre, and unmistakably Apple. That intro now reads like the final act of full-throttle confidence.This year’s WWDC offered a particularly crisp contrast. Aside from the new intro — which features Craig Federighi drifting an F1-style race car across the inner rooftop ring of Apple Park as a “therapy session”, a not-so-subtle nod to the upcoming Formula 1 blockbuster but also to the accountability for the failure to deliver the system-wide AI on time — WWDC 2025 pulled back dramatically. The new “Apple Intelligence” was introduced in a keynote with zero stumbles, zero awkward transitions, and visuals so pristine they could have been rendered on a Vision Pro. Not only had the scope of WWDC been trimmed down to safer talking points, but even the tone had shifted — less like a tech summit, more like a handsomely lit containment-mode seminar. And that, perhaps, was the problem. The presentation wasn’t a reveal — it was a performance. And performances can be edited in post. Demos can’t.So when Apple in march 2025 quietly admitted, for the first time, in a formal press release addressed to reporters like John Gruber, that the personalized Siri and system-wide AI features would be delayed — the reaction wasn’t outrage. It was something subtler: disillusionment. Gruber’s response cracked the façade wide open. His post opened a slow but persistent wave of unease, rippling through developer Slack channels and private comment threads alike. John Gruber’s reaction, published under the headline “Something is rotten in the State of Cupertino”, was devastating. His critique opened the floodgates to a wave of murmurs and public unease among developers and insiders, many of whom had begun to question what was really happening at the helm of key divisions central to Apple’s future.Many still believe Apple is the only company truly capable of pulling off hardware-software integrated AI at scale. But there’s a sense that the company is now operating in damage-control mode. The delay didn’t just push back a feature — it disrupted the entire strategic arc of WWDC 2025. What could have been a milestone in system-level AI became a cautious sidestep, repackaged through visual polish and feature tweaks. The result: a presentation focused on UI refinements and safe bets, far removed from the sweeping revolution that had been teased as the main selling point for promoting the iPhone 16 launch, “Built for Apple Intelligence”.That tension surfaced during Joanna Stern’s recent live interview with Craig Federighi and Greg Joswiak. These are two of Apple’s most media-savvy execs, and yet, in a setting where questions weren’t scripted, you could see the seams. Their usual fluency gave way to something stiffer. More careful. Less certain. And even the absences speak volumes: for the first time in a decade, no one from Apple’s top team joined John Gruber’s Talk Show at WWDC. It wasn’t a scheduling fluke — nor a petty retaliation for Gruber’s damning March article. It was a retreat — one that Stratechery’s Ben Thompson described as exactly that: a strategic fallback, not a brave reset.Meanwhile, the keynote narrative quietly shifted from AI ambition to UI innovation: new visual effects, tighter integration, call screening. Credit here goes to Alan Dye — Apple VP of Human Interface Design and one of the last remaining members of Jony Ive’s inner circle not yet absorbed into LoveFrom — whose long-arc work on interface aesthetics, from the early stages of the Dynamic Island onward, is finally starting to click into place. This is classic Apple: refinement as substance, design as coherence. But it was meant to be the cherry on top of a much deeper AI-system transformation — not the whole sundae. All useful. All safe. And yet, the thing that Apple could uniquely deliver — a seamless, deeply integrated, user-controlled and privacy-safe Apple Intelligence — is now the thing it seems most reluctant to show.There is no doubt the groundwork has been laid. And to Apple’s credit, Jason Snell notes that the company is shifting gears, scaling ambitions to something that feels more tangible. But in scaling back the risk, something else has been scaled back too: the willingness to look your audience of stakeholders, developers and users live, in the eye, and show the future for how you have carefully crafted it and how you can put it in the market immediately, or in mere weeks. Showing things as they are, or as they will be very soon. Rehearsed, yes, but never faked.Even James Dyson’s live demo of a new vacuum showed more courage. No camera cuts. No soft lighting. Just a human being, showing a thing. It might have sucked, literally or figuratively. But it didn’t. And it stuck. That’s what feels missing in Cupertino.Some have started using the term glasslighting — a coined pun blending Apple’s signature glassy aesthetics with the soft manipulations of marketing, like a gentle fog of polished perfection that leaves expectations quietly disoriented. It’s not deception. It’s damage control. But that instinct, understandable as it is, doesn’t build momentum. It builds inertia. And inertia doesn’t sell intelligence. It only delays the reckoning.Before the curtain falls, it’s hard not to revisit the uncanny polish of Apple’s speakers presence. One might start to wonder whether Apple is really late on AI — or whether it’s simply developed such a hyper-advanced internal model that its leadership team has been replaced by real-time human avatars, flawlessly animated, fed directly by the Neural Engine. Not the constrained humanity of two floating eyes behind an Apple Vision headset, but full-on flawless embodiment — if this is Apple’s augmented AI at work, it may be the only undisclosed and underpromised demo actually shipping.OS30 live demoMeanwhile, just as Apple was soft-pedaling its A.I. story with maximum visual polish, a very different tone landed from across the bay: Sam Altman and Jony Ive, sitting in a bar, talking about the future.https://medium.com/media/5cdea73d7fde0b538e038af1990afa44/hrefNo stage. No teleprompter. No uncanny valley. Just two “old friends”, with one hell of a budget, quietly sketching the next era of computing. A vision Apple once claimed effortlessly.There’s still the question of whether Apple, as many hope, can reclaim — and lock down — that leadership for itself. A healthy dose of competition, at the very least, can only help.Too big, fail too was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.