1/5 🚀 Thrilled to open-source OSCAR 🤖 — an action-conditioned world model for robotics, led by the visiting student in my group @wuzy2115! It generalizes across different robot embodiments with precise action controllability. All trained on a single GH200 GPU, and outperforms existing open-sourced baselines, which have larger model capacity and need more compute. Everything is public, including training data. 📄 Paper: arxiv.org/abs/2606.04463 🌐 Project: wuzy2115.github.io/oscar-pro… 💻 Code: github.com/wuzy2115/oscar-pu… 🤗 Robot data: huggingface.co/datasets/zywu… 🤗 Human data: huggingface.co/datasets/zywu… 🤗 Weights: huggingface.co/zywu2115/OSCA… #Robotics #WorldModels #AI #OpenSource

In the summer of 2023, I cold emailed Jensen Huang and asked to capture a NeRF of him at SIGGRAPH. He responded in about an hour and said yes. A radiance field is, in the simplest terms, akin to a 3D photograph. A moment in time, so completely reconstructed that you can move through it and see it from angles the original cameras never occupied. NeRFs were the original method. Gaussian splatting, which debuted at that same SIGGRAPH, has since become the dominant form of radiance field. I called my late friend James, who told me we needed to begin practicing immediately. We ran capture after capture for weeks until we consistently got the capture time down to ~30 seconds with one camera. Later, in a hallway at the LA Convention Center during SIGGRAPH, I captured the portrait you're seeing now, a full 360° gaussian splat of Jensen, rendered here as a 2D flythrough. Afterward, I continued the conversation with him and members of his team to make the case for radiance fields as a foundational representation for imaging. To my surprise, they listened. Three years later, NVIDIA has several works, including NuRec, fVDB, 3DGRUT, and gsplat all utilizing radiance fields. The landscape has evolved enough that the reasoning is obvious. Gaussian splatting has begun to ship across some of the world’s largest industries, including autonomous vehicles, AEC, geospatial, media and entertainment, robotics, e-commerce, hospitality. It’s become clear that lifelike 3D is here to stay. And yet I think we will look back and be disappointed by how late we started taking 3D portraits of the people around us, just like how we have sparse 2D photos of our grandparents and great grandparents. We have billions of photographs of the people we know and love, but almost no radiance fields of them. I'll be returning to SIGGRAPH in LA where this was initially captured three years ago, with the landscape looking significantly different. Radiance fields are more under deployed than ever relative to what they can do. I'm excited for the future of imaging, and for 2D to transition into 3D. I have a few things up my sleeve that I think will make that case plainly.

how did people even learn to code when there was no docs, no YouTube... nothing?

Reframing a photo after it’s been taken with Spatial Reframing ✨ The new iOS 27 Apple Intelligence feature demo (Beta) #WWDC #iOS27

Made me laugh way to hard #CVPR2026

Having a few hours this afternoon after @CVPR? Consider visiting Red Rocks 20 minutes outside of Denver! For some inspiration, here's a world processed by @SpAItial_AI Echo-2 HQ model from a single phone image! Link to the world in🧵

Excited to release v0 of SO-101 Bench, a benchmark with 4 tasks designed to measure various core capabilities of robot foundation models. It includes precise spatial and geometric constraints, novel/unusual objects, temporarily occluded objects, and often requires visuospatial planning. It is presented here as a real-world case study, then a scalable real2sim evaluation suite, all executed on the cheap and open-sourced SO-101 arm!