Demo: FreePose: Real-Time View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement

3D human pose estimation is a key technology for applications like healthcare and robotics, but its performance in real-world deployments is often compromised by viewpoint variations. We propose FreePose, a novel framework that achieves viewpoint invariance by explicitly disentangling motion and view features. The core of FreePose is a lightweight view estimator that predicts camera viewpoint from intermediate pose features. This information is then used to guide robust feature alignment and enable a view-aware inference pipeline that adaptively optimizes for latency on edge devices. We will demonstrate our system using a single Intel RealSense D435 camera, capturing from varying viewpoints throughout the demo, with real-time pose inference performed on a PC and an NVIDIA Jetson Orin NX. By leveraging FreePose, our framework achieves consistent accuracy and high frame rates across varying camera angles and positions. A video demonstration of FreePose's performance is available at https://youtu.be/tDIpCcbaRXc.