body-models 0.2.0

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body-models

A unified Python library for parametric human body models. Provides a common interface across SMPL, SMPL-X, SKEL, FLAME, ANNY, and MHR body models.

Installation

pip install body-models

Or with uv:

uv add body-models

Optional backends

PyTorch and JAX are optional dependencies. Install them for the corresponding backends:

# For PyTorch backend
pip install body-models[torch]

# For JAX backend
pip install body-models[jax]

Note: MHR model requires PyTorch for loading the model file (stored in .pt format).

Model Setup

Auto-download models

ANNY and MHR models are automatically downloaded on first use:

from body_models.anny.torch import ANNY
from body_models.mhr.torch import MHR

model = ANNY()  # Downloads automatically (CC0 license)
model = MHR()   # Downloads automatically (Apache 2.0)

Registration-required models

SMPL, SMPL-X, SKEL, and FLAME require registration. Download from:

• SMPL: https://smpl.is.tue.mpg.de/
• SMPL-X: https://smpl-x.is.tue.mpg.de/
• SKEL: https://skel.is.tue.mpg.de/
• FLAME: https://flame.is.tue.mpg.de/

For SMPL, convert the pkl file to npz format (the pkl files use chumpy):

convert-smpl-pkl /path/to/model.pkl /path/to/model.npz

Then configure the paths (per gender):

body-models set smpl-neutral /path/to/SMPL_NEUTRAL.npz
body-models set smpl-male /path/to/SMPL_MALE.npz
body-models set smpl-female /path/to/SMPL_FEMALE.npz
body-models set smplx-neutral /path/to/SMPLX_NEUTRAL.npz
body-models set skel /path/to/skel
body-models set flame /path/to/flame

Or pass file paths directly:

from body_models.smpl.torch import SMPL

# Direct file path (no gender needed)
model = SMPL(model_path="/path/to/SMPL_NEUTRAL.npz")

# From config using gender
model = SMPL(gender="neutral")  # Uses smpl-neutral config key

Configuration

View current settings:

$ body-models
Config file: /path/to/config.toml  # Platform-dependent location

Current settings:
  smpl-male: /data/models/smpl/SMPL_MALE.npz
  smpl-female: /data/models/smpl/SMPL_FEMALE.npz
  smpl-neutral: /data/models/smpl/SMPL_NEUTRAL.npz
  smplx-male: (not set)
  smplx-female: (not set)
  smplx-neutral: (not set)
  skel: (not set)
  flame: (not set)
  anny: (not set)
  mhr: (not set)

Manage paths:

body-models set <model> <path>   # Set model path
body-models unset <model>        # Remove from config

Quick Start

# Import from specific backend (torch, numpy, or jax)
from body_models.smplx.torch import SMPLX

# Load model from config
model = SMPLX(gender="neutral")  # Uses smplx-neutral config key

# Or load from direct file path
model = SMPLX(model_path="/path/to/SMPLX_NEUTRAL.npz")

# Get default parameters
params = model.get_rest_pose(batch_size=4)

# Generate mesh vertices
vertices = model.forward_vertices(**params)  # [4, V, 3]

# Get skeleton transforms
skeleton = model.forward_skeleton(**params)  # [4, J, 4, 4]

Available backends:

• body_models.<model>.torch - PyTorch (differentiable)
• body_models.<model>.numpy - NumPy
• body_models.<model>.jax - JAX/Flax (differentiable)

Common Properties

All models inherit from BodyModel and share these properties:

Property	Type	Description
num_joints	int	Number of skeleton joints
num_vertices	int	Number of mesh vertices
faces	[F, 3]	Mesh face indices
skin_weights	[V, J]	Skinning weights
rest_vertices	[V, 3]	Vertices in rest pose

Common Methods

# Get zero-initialized parameters (model-specific keys)
params = model.get_rest_pose(batch_size=1)

# Compute mesh vertices [B, V, 3] in meters
vertices = model.forward_vertices(**params)

# Compute joint transforms [B, J, 4, 4] in meters
transforms = model.forward_skeleton(**params)

Supported Models

SMPL

The original parametric body model with 6890 vertices and 24 joints.

from body_models.smpl.torch import SMPL  # or .numpy, .jax

model = SMPL(gender="neutral")  # "neutral", "male", or "female"

vertices = model.forward_vertices(
    shape,               # [B, 10] body shape betas
    body_pose,           # [B, 23, 3] axis-angle per joint
    pelvis_rotation,     # [B, 3] root joint rotation (optional)
    global_rotation,     # [B, 3] post-transform rotation (optional)
    global_translation,  # [B, 3] translation (optional)
)

Conversion functions for working with the official smplx library format:

from body_models import smpl

# Convert flat tensors to API format
args = smpl.from_native_args(shape, body_pose, pelvis_rotation, global_translation)
vertices = model.forward_vertices(**args)
transforms = model.forward_skeleton(**args)

# Convert outputs back to native format (removes feet offset, extracts joint positions)
result = smpl.to_native_outputs(vertices, transforms, model._feet_offset)

SMPL-X

Expressive body model with articulated hands and facial expressions.

from body_models.smplx.torch import SMPLX  # or .numpy, .jax

model = SMPLX(
    gender="neutral",     # "neutral", "male", or "female"
    flat_hand_mean=False, # Flat hands as mean pose
)

vertices = model.forward_vertices(
    shape,               # [B, 10] body shape betas
    body_pose,           # [B, 21, 3] axis-angle per body joint
    hand_pose,           # [B, 30, 3] axis-angle (left 15 + right 15)
    head_pose,           # [B, 3, 3] jaw + left eye + right eye
    expression,          # [B, 10] facial expression (optional)
    pelvis_rotation,     # [B, 3] root joint rotation (optional)
    global_rotation,     # [B, 3] post-transform rotation (optional)
    global_translation,  # [B, 3] translation (optional)
)

Conversion functions for working with the official smplx library format:

from body_models import smplx

# Convert flat tensors to API format
args = smplx.from_native_args(shape, expression, body_pose, hand_pose, head_pose,
                              pelvis_rotation, global_translation)
vertices = model.forward_vertices(**args)
transforms = model.forward_skeleton(**args)

# Convert outputs back to native format
result = smplx.to_native_outputs(vertices, transforms, model._feet_offset)

SKEL

Anatomically realistic skeletal articulation based on OpenSim. Only "male" and "female" genders are supported (no "neutral").

from body_models.skel.torch import SKEL  # or .numpy, .jax

model = SKEL(gender="male")  # "male" or "female" (no neutral)

vertices = model.forward_vertices(
    shape,               # [B, 10] body shape betas
    pose,                # [B, 46] anatomically constrained DOFs
    global_rotation,     # [B, 3] axis-angle (optional)
    global_translation,  # [B, 3] (optional)
)

FLAME

FLAME (Faces Learned with an Articulated Model and Expressions) head model.

from body_models.flame.torch import FLAME  # or .numpy, .jax

model = FLAME()  # Uses configured path

vertices = model.forward_vertices(
    shape,               # [B, 300] shape betas (can use fewer)
    expression,          # [B, 100] expression coefficients (optional)
    pose,                # [B, 4, 3] axis-angle for neck, jaw, left_eye, right_eye (optional)
    head_rotation,       # [B, 3] root joint rotation (optional)
    global_rotation,     # [B, 3] post-transform rotation (optional)
    global_translation,  # [B, 3] translation (optional)
)

Conversion functions for working with the official FLAME/smplx library format:

from body_models import flame

# Convert native args to API format
args = flame.from_native_args(shape, expression, pose, head_rotation, global_rotation, global_translation)
vertices = model.forward_vertices(**args)
transforms = model.forward_skeleton(**args)

# Convert outputs back to native format
result = flame.to_native_outputs(vertices, transforms)

ANNY

Phenotype-based body model with intuitive shape parameters.

from body_models.anny.torch import ANNY  # or .numpy, .jax

model = ANNY(
    rig="default",                # "default", "default_no_toes", "cmu_mb", "game_engine", "mixamo"
    topology="default",           # "default" or "makehuman"
    all_phenotypes=False,         # Include race/cupsize/firmness
    extrapolate_phenotypes=False, # Allow values outside [0, 1]
)

vertices = model.forward_vertices(
    gender,              # [B] in [0, 1] (0=male, 1=female)
    age,                 # [B] in [0, 1]
    muscle,              # [B] in [0, 1]
    weight,              # [B] in [0, 1]
    height,              # [B] in [0, 1]
    proportions,         # [B] in [0, 1]
    pose,                # [B, J, 3] axis-angle per joint
    global_rotation,     # [B, 3] axis-angle (optional)
    global_translation,  # [B, 3] (optional)
)

MHR

Meta Human Renderer with neural pose correctives. Requires PyTorch for model loading.

from body_models.mhr.torch import MHR  # or .numpy, .jax (all require torch for loading)

model = MHR(lod=1)  # Level of detail

vertices = model.forward_vertices(
    shape,               # [B, 45] identity blendshapes
    pose,                # [B, 204] pose parameters
    expression,          # [B, 72] facial expression (optional)
    global_rotation,     # [B, 3] axis-angle (optional)
    global_translation,  # [B, 3] (optional)
)

Conversion functions for working with the original MHR format (cm units):

from body_models import mhr

# Convert native args (shape, expression, pose order) to API format
args = mhr.from_native_args(shape, expression, pose)
vertices = model.forward_vertices(**args)
transforms = model.forward_skeleton(**args)

# Convert outputs to native format (cm units, skeleton state [t, q, s])
result = mhr.to_native_outputs(vertices, transforms)

Coordinate System

The unified API returns outputs in:

• Y-up coordinate system
• Meters as the unit
• Feet at floor level (Y=0)

Use the to_native_outputs() conversion functions to get outputs in the original library conventions.

Development

uvx ruff format .   # Format code
uvx ruff check .    # Lint
uvx ty check        # Type check

License

See individual model licenses for usage terms:

• SMPL: https://smpl.is.tue.mpg.de/
• SMPL-X: https://smpl-x.is.tue.mpg.de/
• SKEL: https://skel.is.tue.mpg.de/
• FLAME: https://flame.is.tue.mpg.de/
• ANNY: CC0 (MakeHuman data)
• MHR: Apache 2.0 (Meta Platforms, Inc.)