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Excited to be at #ICLR2025 in person this year! Looking forward to reconnecting and making new friends.🤩 Come chat with us about Dynamic Gaussians Mesh at poster #97 tomorrow (4/26, 3–5:30pm). See you there!🥳 Website: liuisabella.com/DG-Mesh

I’ll be at the robot learning workshop today and giving an oral talk at 9:15 AM on ManiSkill3 in rooms Garnet 216/217 at ICLR. Come see the crazy things you can do with fast sim+rendering like fast visual RL and zero-shot RGB sim2real! ManiSkill was also accepted at RSS!

Supervised learning has held 3D Vision back for too long. Meet RayZer — a self-supervised 3D model trained with zero 3D labels: ❌ No supervision of camera & geometry ✅ Just RGB images And the wild part? RayZer outperforms supervised methods (as 3D labels from COLMAP is noisy)

Meet 𝐀𝐌𝐎 — our universal whole‑body controller that unleashes the 𝐟𝐮𝐥𝐥  kinematic workspace of humanoid robots to the physical world. AMO is a single policy trained with RL + Hybrid Mocap & Trajectory‑Opt. Accepted to #RSS2025. Try our open models & more 👉

On my way to ICRA! Our group will be presenting Mobile-TeleVision (below) and WildMA (wildlma.github.io). Looking forward to chatting!

🚀 Struggling with the lack of high-quality data for AI-driven human-object interaction research? We've got you covered! Introducing HUMOTO, a groundbreaking 4D dataset for human-object interaction, developed with a combination of wearable motion capture, SOTA 6D pose

🧠 Can a single robot policy control many, even unseen, robot bodies? We scaled training to 1000+ embodiments and found: More training bodies → better generalization to unseen ones. We call it: Embodiment Scaling Laws. A new axis for scaling. 🔗 embodiment-scaling-laws.github.io 🧵👇

For years, I’ve been tuning parameters for robot designs and controllers on specific tasks. Now we can automate this on dataset-scale. Introducing Co-Design of Soft Gripper with Neural Physics - a soft gripper trained in simulation to deform while handling load.

What if a Transformer could render? Not text → image. But mesh → image — with global illumination. No rasterizers. No ray-tracers. Just a Transformer without per-scene training. RenderFormer does exactly that. #SIGGRAPH2025 🔗microsoft.github.io/renderformer

Bored of linear recurrent memories (e.g., linear attention) and want a scalable, nonlinear alternative? Our new paper “Test-Time Training Done Right” propose LaCT (Large Chunk Test-Time Training) — a highly efficient, massively scalable nonlinear memory with: 💡 Pure PyTorch

The code of GSPN #CVPR2025 is released! We proposed a new sqrt(N) complexity attention mechanism, which enables efficient high resolution image generation. We can generate 8k images with 42x speed up compared to self-attention in StableDiffusionXL! Code: github.com/NVlabs/GSPN

🚀 Excited to announce our CVPR 2025 Workshop: 3D Digital Twin: Progress, Challenges, and Future Directions 🗓 June 12, 2025 · 9:00 AM–5:00 PM 📢 Incredible lineup: Richard Newcombe, Andrea Vedaldi Visual Geometry Group (VGG),Hao (Richard) Zhang,Qianqian Wang,Dr. Xiaoshuai Zhang Hillbot,

Welcome to join us on June 12th AM sessions! Location and time are shown in the image 😃 Thanks for the speakers and organizers : ) #CVPR2025

🥳 EditAR code is released! Welcome to check it out. 👉Presenting EditAR at #CVPR2025! (Friday afternoon, Jun 13, 4:00pm-6:00pm, Hall D #242) Code: github.com/JitengMu/EditAR Project: jitengmu.github.io/EditAR

📢 We’re presenting two posters at #CVPR2025 today! 🗓️ June 13 | 🕓 16:00–18:00 | 📍 Exhibit Hall D 🔹 Genfusion — Booth 61 🔹 Feat2GS — Booth 93 Come by to chat about generative 3D, geometry, and beyond. See you there! #CVPR25 #3Dvision #AI

Check out 🌟Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry & Physics for Mesh-Free Simulation #CVPR2025, from Lingjie Liu’s lab at UPenn. Congrats to Chuhao Chen! Vid2Sim aims to achieve system identification by reconstructing geometry, appearance,

🚀Heading to #RSS2025? Swing by EEB 248 on Wednesday, June 25 at 3:30 PM for a live demo of our data-driven, co-design soft gripper 🥢 at the workshop Robot Hardware-Aware Intelligence!

How to generate billion-scale manipulation demonstrations easily? Let us leverage generative models! 🤖✨ We introduce Dex1B, a framework that generates 1 BILLION diverse dexterous hand demonstrations for both grasping 🖐️and articulation 💻 tasks using a simple C-VAE model.