pybvh

A lightweight Python library for reading, writing, and manipulating BVH motion capture files. Built for researchers and developers working with skeletal animation and motion data.

Documentation · Quick Start · Feature Gallery — every feature, one picture each · Find a function · Tutorials

Features

• Read & write BVH files with full hierarchy and motion data preservation — I/O
• Rotation conversions between Euler angles, rotation matrices, quaternions, 6D (Zhou et al.), and axis-angle — all vectorized with NumPy — guide
• Forward kinematics to compute 3D joint positions from angles — core concepts
• Skeleton operations: retargeting, scaling, joint extraction, Euler order changes — guide
• Frame operations: slicing, concatenation, resampling to different frame rates — guide
• Spatial transforms: mirroring, vertical rotation, speed perturbation, joint noise, root translation, frame dropout — all with seeded randomization — guide
• Motion analysis: joint velocities/accelerations, root trajectory, foot contact detection, gait parameters, and a one-stop to_feature_array() export — guide
• Motion descriptors: trajectory geometry (curvature, torsion, path length, bounding volumes, centre of mass), dynamics (jerk, smoothness/SPARC, kinetic energy, gait), and SE(3) rigid-transform math (twists, screw interpolation, geodesic distance) — all pure NumPy — guide · gallery
• Signal utilities (pybvh.signal): finite differences, temporal statistics, smoothing, FFT/dominant frequency, polyline simplification — API
• Batch loading of entire directories with optional parallel I/O — guide
• Pandas ready — to_df_dict() output drops straight into pd.DataFrame — guide
• 3D visualization with multiple backends (matplotlib, OpenCV, k3d, vedo) — API

Philosophy

pybvh is framework-agnostic and outputs pure NumPy arrays. It understands motion capture data but does not assume what you'll do with it — the same library serves ML researchers, biomechanics scientists, and game developers. For ML-specific features (tensor packing, PyTorch Datasets, augmentation pipelines), see the companion library pybvh-ml (documentation).

Installation

pip install pybvh

Quick Start

import pybvh

# Load a BVH file (pybvh.Bvh.from_file("walk.bvh") is the classmethod spelling)
bvh = pybvh.read_bvh_file("walk.bvh")
print(bvh)  # "24 joints, 75 frames at 30.0 fps (frame_time=0.033333s, from walk.bvh)"

# Access motion data as NumPy arrays
bvh.root_pos          # (F, 3) root translation per frame
bvh.joint_angles      # (F, J, 3) Euler angles in radians
bvh.joint_names       # ['Hips', 'Spine', ...] (excludes end sites)

# Get 3D joint positions via forward kinematics
coords = bvh.node_positions()  # (F, N, 3)

# Convert to other rotation representations
root_pos, quats = bvh.to_quat()          # (F, 3), (F, J, 4)
root_pos, rot6d = bvh.to_6d()            # (F, 3), (F, J, 6)

# Write back to file
bvh.write("output.bvh")

Motion Analysis

vel = bvh.joint_velocities()    # (F, J, 3) in units/second
contacts = bvh.foot_contacts()  # (F, num_feet) binary labels, feet auto-detected

# One-stop export — flat feature array for ML pipelines
features = bvh.to_feature_array(
    representation="6d",
    include_velocities=True,
    include_foot_contacts=True,
)  # (F, D)

Beyond the basics sits a full descriptor layer — curvature, smoothness (SPARC), kinetic energy, gait parameters, SE(3) twists — each drawn with its exact call in the Feature Gallery and explained in the Motion Descriptors guide.

Visualization

bvh.plot_rest_pose()                             # T-pose
bvh.plot_frame(frame=0, camera="front")          # also "side", "top", (azim, elev)
bvh.plot_trajectory()                            # 2D top-down root path
bvh.render("walk.mp4")                           # video/GIF/HTML export
bvh.render("walk.mp4", follow=True)              # camera tracks character as it turns
bvh.play()                                       # interactive playback (auto-detects backend)

pip install pybvh[opencv]       # Fast video rendering
pip install pybvh[interactive]  # k3d for Jupyter notebooks
pip install pybvh[viewer]       # vedo for desktop interactive viewer
pip install pybvh[all-viz]      # All of the above

Multi-skeleton comparison, camera control, and backend details: Visualization API.

More

Tutorials

Eight Jupyter notebooks with detailed walkthroughs, from reading your first file to motion descriptors — see the tutorials page. Each tutorial is committed as a Jupytext-paired .ipynb + .py so the source is reviewable as plain Python.

Stability and versioning

pybvh is in 0.x — expect breaking changes between minor versions.

We treat 0.x as design space: when a past choice turns out to be wrong, we fix it at the root rather than carry scar tissue forward. No deprecation cycles, no compatibility shims; each release ships a single clean migration path, documented in the CHANGELOG. If you depend on pybvh from production code, pin to an exact version (pybvh==0.8.0) and read the upgrade notes before bumping.

This will change at 1.0: from then on, pybvh will commit to strict semver — no breaking changes within a major version, deprecation warnings (at least one minor release) before any future removal. Until 1.0, "make the library better" wins over "preserve the old behavior."

Requirements

• Python >= 3.9
• NumPy >= 1.21
• Matplotlib >= 3.7

Pandas is optional (pip install "pybvh[pandas]") - only used in the tutorials, not part of pybvh library.

License

MIT