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ZuoCX1996 / Loose-Inertial-Poser

Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket
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Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket

Implementation of our CVPR 2024 paper "Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket". Including network weights, training and evaluation scripts.

Run train_SemoAE.py to train SeMo-AE.

Run train_poser.py to train pose estimation network.

Run evaluation.py to get ang Err and pos Err.

Live Demo

  1. Prepare the pose visualization unity project. You can find it at TransPose project page: https://github.com/Xinyu-Yi/TransPose
  2. Run live demo.py
  3. Run unity visualization

LIP Dataset

The real-world dataset is in the folder named LIP_Dataset, please notice that the original data frame rate is 60Hz, and it was downsampled to 30Hz in our implementation.

The original data frame rate is 60Hz, and it was down sampled to 30Hz which is a 50% reduction.

Synthesized IMU data

The synthesized IMU data using TailorNet is available at the following links:

Baidu Cloud: https://pan.baidu.com/s/1UmFCHvt3pqIYixuuCqBqWg?pwd=nmyr

OneDrive: https://1drv.ms/f/c/d6cd78cd7ce83043/Etl3HdtmHl5Dn05NQ-ZKScMBYsCo4gTef7Io3JVZb07nxQ?e=UMXx0e

The synthesized IMU data ON AMASS (Match the TailorNet syn IMU data) is available at the following links: : https://1drv.ms/f/c/d6cd78cd7ce83043/Eqz0kup3E0NEpr0j0A9R8DIBb4Jr0D-PS2MnQtYKJjCdzQ?e=urBJ6m