Kinematic Hand Skeleton Optimization in Jax
MVP of Multiview Video → Camera parameters + 3D keypoints. The basic pipeline as of right now looks like this: 1. Capture – Using 4 iPhones and an Insta360 Go. iPhone videos are captured via Final Cut Pro Multicam for easy sync and the exocentric view; the Insta360 Go is used for the egocentric view. 2. Sync – Custom Gradio app using two @rerundotio viewers and callbacks for easily aligning frame timestamps so the ego and exo views are aligned. 3. Calibrate – Use VGGT from Meta to get intrinsics/extrinsics for sparse cameras. 4. Estimate 3D – Use RTMLib whole‑body keypoint estimator on…
Kinematic Hand Skeleton Optimization in Jax launched on May 2, 2025 with 20 votes, #831 of 7,905 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Kinematic Hand Skeleton Optimization in JAX is a fully-differentiable hand skeleton model built in JAX for studying forward and backward kinematics in imitation learning. It visualizes joint angles and their effects on skeletal movement through an interactive Jupyter Notebook using Rerun's callback system. The tool is designed for researchers and developers working with robotic hand control and kinematic learning problems.
written from the facts on this page · September 2026
From the sources
Contribute to rerun-io/pi0-lerobot development by creating an account on GitHub.
git clone https://github.com/rerun-io/pi0-lerobot.git cd pi0-lerobot Usage Camera Calibration using VGGT For HOCap dataset, sample is automatically downloaded from dataset on command run. Checkout example rrd file here The goal is to create a pipeline that converts time-synced multi-camera footage into axis-angle joint angles (θ) and metric 3D positions for every finger joint. This provides "motion fuel" for robotic retargeting and learning fine motor skills. Use a grpc server to collect sensor data with an arkit/arcore capable device via ARFlow. Download Phone code here and install on device then run arflowfrom github.com
In the maker’s words, at launch
I've been trying to wrap my head around fwd/bwd kinematics for imitation learning, so I built a fully‑differentiable kinematic hand skeleton in JAX and visualized it with reruns new callback system in a Jupyter Notebook. This shows each joint angle and how it impacts the kinematic skeleton.
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