Skeleton Operations¶

Euler order conversion¶

# Change all joints at once
bvh_xyz = bvh.change_euler_order("XYZ")

# Change a single joint only
bvh_single = bvh.change_euler_order("XYZ", joint="Hips")

Skeleton scaling¶

bvh_scaled = bvh.scale(0.01)   # uniform — scales offsets AND root translation

Retargeting¶

reference = pybvh.read_bvh_file("reference_skeleton.bvh")
bvh_retarget = bvh.retarget(reference)

# With name mapping (when joint names differ)
bvh_retarget = bvh.retarget(reference, name_mapping={
    "Hips": "pelvis", "Spine": "spine_01"
})

Joint extraction¶

upper = bvh.extract_joints(["Hips", "Spine", "Neck", "Head"])

The three skeleton operations on one shared scale — scaled to half, retargeted back to the original proportions, and reduced to 11 joints:

Four skeleton variants drawn at one shared scale: original, half-size scale, a tall clip retargeted to the original proportions, and an 11-joint extraction

Frame operations¶

clip = bvh[10:50]              # frame slicing (steps work too: bvh[::2])
combined = bvh + other_bvh     # concatenation (same skeleton required)
bvh_30fps = bvh.resample(30)

Pandas integration¶

to_df_dict() exports the motion as labeled columns; to_hierarchy_dict() exports the skeleton as a plain dict. Together they carry everything needed to rebuild the Bvh with Bvh.from_df(hier, df) — hier accepts either the hierarchy dict or a bvh.nodes list (the module-level pybvh.df_to_bvh is the same function):

import pandas as pd
from pybvh import Bvh

df = pd.DataFrame(bvh.to_df_dict(mode="euler"))
hier = bvh.to_hierarchy_dict()   # {name: {offset, parent, rot_channels, ...}}

bvh_from_df = Bvh.from_df(hier, df)

Inplace convention¶

All mutation methods default to inplace=False (return a new Bvh):

bvh2 = bvh.scale(0.01)                     # new object
bvh.scale(0.01, inplace=True)              # modifies self, returns None