poselib-v2 0.0.1

Library for loading, manipulating, and retargeting skeleton poses and motions.

poselib

poselib is a library for loading, manipulating, and retargeting skeleton poses and motions. It is separated into three modules: poselib.core for basic data loading and tensor operations, poselib.skeleton for higher-level skeleton operations, and poselib.visualization for displaying skeleton poses. This library is built on top of the PyTorch framework and requires data to be in PyTorch tensors.

poselib.core

• poselib.core.rotation3d: A set of Torch JIT functions for computing quaternions, transforms, and rotation/transformation matrices.
  • quat_* manipulate and create quaternions in [x, y, z, w] format (where w is the real component).
  • transform_* handle 7D transforms in [quat, pos] format.
  • rot_matrix_* handle 3x3 rotation matrices.
  • euclidean_* handle 4x4 Euclidean transformation matrices.
• poselib.core.tensor_utils: Provides loading and saving functions for PyTorch tensors.

poselib.skeleton

• poselib.skeleton.skeleton3d: Utilities for loading and manipulating skeleton poses, and retargeting poses to different skeletons.
  • SkeletonTree is a class that stores a skeleton as a tree structure. This describes the skeleton topology and joints.
  • SkeletonState describes the static state of a skeleton, and provides both global and local joint angles.
  • SkeletonMotion describes a time-series of skeleton states and provides utilities for computing joint velocities.

poselib.visualization

• poselib.visualization.common: Functions used for visualizing skeletons interactively in matplotlib.
  • In SkeletonState visualization, use key q to quit window.
  • In interactive SkeletonMotion visualization, you can use the following key commands:
    • w - loop animation
    • x - play/pause animation
    • z - previous frame
    • c - next frame
    • n - quit window

Key Features

Poselib provides several key features for working with animation data. We list some of the frequently used ones here, and provide instructions and examples on their usage.

Importing from FBX

Poselib supports importing skeletal animation sequences from .fbx format into a SkeletonMotion representation. To use this functionality, you will need to first set up the Python FBX SDK on your machine using the following instructions.

This package is necessary to read data from fbx files, which is a proprietary file format owned by Autodesk. The latest FBX SDK tested was FBX SDK 2020.2.1 for Python 3.7, which can be found on the Autodesk website: https://www.autodesk.com/developer-network/platform-technologies/fbx-sdk-2020-2-1.

Follow the instructions at https://help.autodesk.com/view/FBX/2020/ENU/?guid=FBX_Developer_Help_scripting_with_python_fbx_installing_python_fbx_html for download, install, and copy/paste instructions for the FBX Python SDK.

This repo provides an example script fbx_importer.py that shows usage of importing a .fbx file. Note that SkeletonMotion.from_fbx() takes in an optional parameter root_joint, which can be used to specify a joint in the skeleton tree as the root joint. If root_joint is not specified, we will default to using the first node in the FBX scene that contains animation data.

Importing from MJCF

MJCF is a robotics file format supported by Isaac Gym. For convenience, we provide an API for importing MJCF assets into SkeletonTree definitions to represent the skeleton topology. An example script mjcf_importer.py is provided to show usage of this.

This can be helpful if motion sequences need to be retargeted to your simulation skeleton that's been created in MJCF format. Importing the file to SkeletonTree format will allow you to generate T-poses or other retargeting poses that can be used for retargeting. We also show an example of creating a T-Pose for our AMP Humanoid asset in generate_amp_humanoid_tpose.py.

Retargeting Motions

Retargeting motions is important when your source data uses skeletons that have different morphologies than your target skeletons. We provide APIs for performing retarget of motion sequences in our SkeletonState and SkeletonMotion classes.

To use the retargeting API, users must provide the following information:

• source_motion: a SkeletonMotion npy representation of a motion sequence. The motion clip should use the same skeleton as the source T-Pose skeleton.
• target_motion_path: path to save the retargeted motion to
• source_tpose: a SkeletonState npy representation of the source skeleton in it's T-Pose state
• target_tpose: a SkeletonState npy representation of the target skeleton in it's T-Pose state (pose should match source T-Pose)
• joint_mapping: mapping of joint names from source to target
• rotation: root rotation offset from source to target skeleton (for transforming across different orientation axes), represented as a quaternion in XYZW order.
• scale: scale offset from source to target skeleton

We provide an example script retarget_motion.py to demonstrate usage of the retargeting API for the CMU Motion Capture Database. Note that the retargeting data for this script is stored in data/configs/retarget_cmu_to_amp.json.

Additionally, a SkeletonState T-Pose file and retargeting config file are also provided for the SFU Motion Capture Database. These can be found at data/sfu_tpose.npy and data/configs/retarget_sfu_to_amp.json.

Documentation

We provide a description of the functions and classes available in poselib in the comments of the APIs. Please check them out for more details.