Carnegie-Mellon Graphics Lab Motion Capture Database (fbx conversion)

This is a dataset of motion capture data in the .fbx format. The original dataset (free to use, modify, and share) is created by the CMU Graphics Lab and can be accessed here (http://mocap.cs.cmu.edu/). This version contains the .fbx conversion of the files along with a categorisation of some of the motion data for imitation learning tasks. This can directly be used with the data pipelines described here (https://github.com/anishhdiwan/diffusion_motion_priors) to view, process, and retarget the data for any other task. Please refer to the README in the repository for further documentation.

Compiled from the individual CMU index files by B. Hahne. FBX conversion for Unity by Ward Dewaele from cMonkeys.

CMU Notice

When browsing for motions, start with the higher numbered subjects first. The lower numbers contain some of our earliest motion capture sessions, and may not be as high quality.

This data is free for use in research projects.

You may include this data in commercially-sold products,

but you may not resell this data directly, even in converted form.

If you publish results obtained using this data, we would appreciate it

if you would send the citation to your published paper to jkh+mocap@cs.cmu.edu,

and also would add this text to your acknowledgments section:

The data used in this project was obtained from mocap.cs.cmu.edu.

The database was created with funding from NSF EIA-0196217.

This is an index of subset in CMU motion database. The corresponding ‘*.bvh’ files can be downloaded from http://mocap.cs.cmu.edu/ . Please cite this paper: Qinkun Xiao, Junfang Li, Qinhan Xiao. “Human Motion Capture Data Retrieval Based on Quaternion and EMD”. International Conference on Intelligent Human-Machine Systems and Cybernetics, v 1, 2013, pp. 517–520.

This database a part of CMU Graphics Lab Motion Capture Database.This part only include "walking" move to make motion analysis.The data collected by vicon-marker system.The data format is c3d, asf/amc file.For more information please visit link.

I uploaded this data just for show how to parse c3d/amc/asf files and make some experiment on it.

Here is the link of database.

This is a pre-processed version of the dataset saved in numpy format. The original dataset is obtained from CMU-MOCAP.

The data are 3-dimensional arrays of shape [n_samples, time_steps, n_variables]. The data can be loaded as follows:

loaded_data = np.load("CMU.npz")
Xtr = loaded_data['Xtr'] # Training data of shape (29, 580, 62)
Ytr = loaded_data['Ytr'] # Training labels of shape (29, 1)
Xte = loaded_data['Xte'] # Test data of shape (29, 580, 62)
Yte = loaded_data['Yte'] # Test labels of shape (29, 1)

Neural Policy Style Transfer with Twin-Delayed DDPG (NPST3) dataset. The research leading to these results has received funding from: RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; ROBOASSET, ”Sistemas robóticos inteligentes de diagnóstico y rehabilitación de ter apias de miembro superior”, PID2020-113508RB-I00 funded by AGENCIA ESTATAL DE INVESTIGACION (AEI); and “Programa propio de investigación convocatoria de movilidad 2020” from Universidad Carlos III de Madrid. The original data used in this project was obtained from mocap.cs.cmu.edu. The original data was created with funding from NSF EIA-0196217.