IMO this is a much needed quality-of-life upgrade to the 3D-aware robotics stack for the new paradigm of vision-based robot control
Thanks as always to the great mentor team of @basilevanh@vitorguizilini@yuewang314
Inferring a robot’s 3D state (robot↔camera pose + joint angles / link poses) is still weirdly clunky
Introducing Fiducial Exoskeletons: Image-Centric Robot State Estimation!
https://t.co/4u8wdD8xZ8
FidEx makes it fast + robust from a single image ���
I've found it incredibly useful in my recent experiments -- camera pose and intrinsics from a single image and even moving camera stream, as well as for really easy one-second robot re-calibration
Introducing the USC Physical Superintelligence (PSI) Lab (https://t.co/nACO3kGxdD). We are rebranding to better reflect our current focus. From here on out, we are tackling one thing: solving robotics and physical intelligence with every model, every bug, and every line of code. And yes, we are hiring at all levels, especially PhDs in this cycle and potential PostDocs who are excited about robotics. We hope you can join us in this journey! 1/9
[Hiring!] I am hiring multiple PhDs @CSatUSC@USCViterbi for this cycle. If you're interested in scene representations, neural simulation, generative AI, and robotics, feel free to mention my name in your application (no need to email). For USC masters/undergrads who're interested in our research, feel free to fill in this form https://t.co/PgyUXhwddR.
Introducing “FlowMap”, the first self-supervised, differentiable structure-from-motion method that is competitive with conventional SfM like Colmap!
https://t.co/ZwJGWfdytQ
IMO this solves a major missing piece for internet-scale training of 3D Deep Learning methods.
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I quit PhD (for a day) and opened a boba shop at @MIT - Generative Boba! It’s a huge success - right next to our office so all the AI researchers are enjoying it. Checkout our boba diffusion algorithm in the poster to understand why boba generation is so important to @MIT_CSAIL !
Introducing pixelSplat: feed-forward Gaussian splats from image pairs! Led by @DavidCharatan and @sizhe_lester_li, collaborating with @taiyasaki! We propose a memory-efficient, fast and editable alternative to pixelNeRF based on 3D Gaussian Splatting! https://t.co/j3EaCtFCGR 1/n
I am presenting my paper “RaMP: Self-Supervised Reinforcement Learning that Transfers using Random Features” at poster 1427 from 5-7pm at Neurips 2023! Don’t miss it!
Website: https://t.co/jopV0V8UsC
How can we learn to generate 3D scenes directly with diffusion models if we only have images, no ground-truth 3d scenes? Ayush, Tianwei and George will tell you at our poster “diffusion with Forward Models”, #202!
Ever wondered what the world looks like to your pet dog? Our latest #ICCV2023 paper, Total-Recon, enables embodied view synthesis of deformable scenes from a casual RGBD video: https://t.co/zpMk5r40iQ
Drop by poster #10 on Friday 10:30~12:30pm in Rm. Foyer Sud to know more!
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Check out “Diffusion with Forward Models”. We learn to sample realistic 3D scenes from a single input image. Our models are trained on videos and do not require 3D training data!
https://t.co/xslKZeYVGp
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