clad-body

ISO 8559-1 body measurements for Anny and MHR parametric body models. Six keys are differentiable through PyTorch autograd for gradient-based body fitting.

Anny and MHR give you a 14–18K vertex mesh and nothing to measure it with. SMPL tooling doesn't port over, and the plane-sweep algorithms look simple until you hit convex-hull tape simulation, contour-fragment merging, and ISO-compliant landmark detection for bust/hip/crotch. clad-body is that work, done once — 25 anthropometric measurements over circumferences, lengths, and body composition (volume, mass, BMI, body fat), calibrated against real scan data. It's used in production at Clad for size-aware virtual try-on.

All bodies are normalised to the same coordinate convention: Z-up, metres, XY-centred, feet at Z=0, +Y=front.

Install

pip install clad-body

# With Anny body loader (requires torch)
pip install 'clad-body[anny]'

# With MHR body loader (requires pymomentum)
pip install 'clad-body[mhr]'

# With 4-view rendering
pip install 'clad-body[render]'

Quick start

from clad_body.load import load_anny_from_params
from clad_body.measure import measure

body = load_anny_from_params(params)

m = measure(body)                                  # all measurements
m = measure(body, preset="core")                   # 4: height, bust, waist, hip
m = measure(body, preset="standard")               # 9: + thigh, upperarm, shoulder, sleeve, inseam
m = measure(body, preset="tops")                   # garment-relevant subset
m = measure(body, only=["bust_cm", "hip_cm"])       # specific keys
m = measure(body, tags={"type": "circumference", "region": "leg"})  # tag filter
m = measure(body, render_path="body.png")          # with 4-view render

MHR works the same way:

from clad_body.load import load_mhr_from_params
body = load_mhr_from_params("path/to/sam3d_params.json")
m = measure(body)

Differentiable path — measure_grad (Anny only, experimental)

Under active development. API surface and supported keys may change between minor versions. Six keys are differentiable today; more will follow.

For autograd-based optimization of the body mesh, use measure_grad(body) instead of measure(body). Same input, same key names — but the returned values are PyTorch tensors with autograd history, so you can put them directly into a loss and backprop into the Anny phenotype parameters.

Pass requires_grad=True to load_anny_from_params to create the body with gradient-enabled phenotype tensors (stored on body.phenotype_kwargs):

import torch
from clad_body.load import load_anny_from_params
from clad_body.measure import measure_grad

body = load_anny_from_params(initial_params, requires_grad=True)
optimizer = torch.optim.Adam(list(body.phenotype_kwargs.values()), lr=0.01)

for step in range(500):
    optimizer.zero_grad()
    m = measure_grad(body, only=["waist_cm", "inseam_cm"])
    loss = (m["waist_cm"] - 78.0) ** 2 + (m["inseam_cm"] - 82.0) ** 2
    loss.backward()
    optimizer.step()

Each measure_grad(body) call re-runs the forward pass using body.phenotype_kwargs, so after optimizer.step() updates the tensors the next iteration measures the new mesh. If you only want to optimize a subset of parameters, load without requires_grad=True and enable it per-tensor: body.phenotype_kwargs["height"].requires_grad_(True).

Supported keys and their calibration error vs the ISO reference that measure() uses:

Key	Error vs ISO
height_cm, waist_cm	exact (same loop / extent)
inseam_cm	RMS 0.06 cm, max 0.10 cm
sleeve_length_cm	RMS 0.33 cm, max 0.55 cm
upperarm_cm	≤ 1 cm
thigh_cm	broken — gradient direction only (vertex loop under-reports by 3–6 cm; use measure() for reporting)

Requesting any other key raises ValueError. There is no silent numpy fallback — it would break gradient flow without warning. For non-differentiable keys use measure().

Public API

Import	What
clad_body.load.load_anny_from_params	Load Anny body from phenotype params
clad_body.load.load_mhr_from_params	Load MHR body from SAM 3D Body params
clad_body.load.AnnyBody, MhrBody	Body dataclasses
clad_body.measure.measure	Measure a body (numpy reporting path, ISO 8559-1)
clad_body.measure.measure_grad	Differentiable measurements for autograd loops (Anny only)
clad_body.measure.REGISTRY	All measurement definitions (dict[str, MeasurementDef])
clad_body.measure.list_measurements	Query measurements by tags
clad_body.measure.MeasurementDef	Measurement definition type

Selection

measure() accepts preset, only, tags, exclude. Precedence: only > preset > tags > default ("all"). exclude is applied last. Only runs computation groups needed for the requested keys.

Introspection

from clad_body.measure import REGISTRY, list_measurements

REGISTRY["bust_cm"].description   # self-measurement instructions
REGISTRY["bust_cm"].iso_ref       # "5.3.4"
REGISTRY["bust_cm"].type          # "circumference"

list_measurements(type="circumference", region="leg")   # [thigh, knee, calf]

Measurement registry

Every measurement is tagged across 5 dimensions. Each carries a human-readable description for self-measurement instructions and i18n key mapping.

Tags

Dimension	Values
type	circumference, length, scalar
standard	iso (ISO 8559-1), tailor (industry standard), derived (computed)
region	neck, torso, abdomen, arm, leg, full_body
tier	core > standard > enhanced > fitted (cumulative)
garments	tops, bottoms, dresses, outerwear, underwear

Tier presets

Preset	Count	Adds
core	4	height, bust, waist, hip
standard	9	thigh, upperarm, shoulder_width, sleeve_length, inseam
enhanced	18	neck, underbust, stomach, mass, volume, bmi, body_fat, belly_depth, back_neck_to_waist
fitted/all	25	knee, calf, wrist, crotch_length, front_rise, back_rise, shirt_length

Full measurement table

Garment codes: Tops, Bottoms, Dresses, Outerwear, Underwear.

Contour	Key	Description	ISO	Type	Std	Region	Tier	Grp	Gar
	height_cm	Vertical distance from floor to top of head. Stand erect, feet together.	5.1.1	scalar	iso	full_body	core	A	all
	bust_cm	Horizontal circumference at the fullest part of the chest/bust. Tape under armpits, across bust prominence, level and snug.	5.3.4	circ	iso	torso	core	A	T,D,O,U
	waist_cm	Horizontal circumference at natural waist, midway between lowest rib and hip bone. Tape at navel height, parallel to floor.	5.3.10	circ	iso	torso	core	A	all
	hip_cm	Horizontal circumference at greatest buttock prominence. Feet together, tape around widest part of hips.	5.3.13	circ	iso	abdomen	core	A	B,D,O,U
	thigh_cm	Horizontal circumference at fullest part of upper thigh, just below gluteal fold. Stand with legs slightly apart.	5.3.20	circ	iso	leg	std	B	B
	upperarm_cm	Circumference at fullest part of upper arm, midway between shoulder and elbow. Arm relaxed, not flexed.	5.3.16	circ	iso	arm	std	B	T,O
	shoulder_width_cm	Distance between left and right shoulder points (acromion), measured across back over C7 vertebra.	5.4.2	length	iso	torso	std	C	T,D,O
	sleeve_length_cm	Distance from shoulder point along outside of slightly bent arm, over elbow, to wrist bone. (ISO §5.4.14 + §5.4.15 outer arm length, computed via plane-slice surface walk on rest pose; differentiable runtime is bone chain + linear correction.)	5.7.8	length	iso	arm	std	C	T,O
	inseam_cm	Distance from crotch point straight down to floor. Stand erect, feet slightly apart.	5.1.15	length	iso	leg	std	E	B
	neck_cm	Circumference just below Adam's apple, perpendicular to neck axis. Comfortably snug.	5.3.2	circ	iso	neck	enh	D	T
	underbust_cm	Horizontal circumference directly below breast tissue, at inframammary crease. Bra band size.	5.3.6	circ	iso	torso	enh	A	T,D,U
	stomach_cm	Horizontal circumference at maximum anterior protrusion of abdomen, usually at/below navel.	--	circ	tailor	abdomen	enh	A	T,B
	mass_kg	Total body mass in kilograms.	5.6.1	scalar	iso	full_body	enh	G	--
	volume_m3	Total body volume in cubic metres, from mesh geometry.	--	scalar	derived	full_body	enh	G	--
	bmi	Body mass index: mass (kg) / height (m)^2.	--	scalar	derived	full_body	enh	G	--
	body_fat_pct	Estimated body fat % via Navy/Weltman equations from circumferences.	--	scalar	derived	full_body	enh	G	--
	belly_depth_cm	How much belly protrudes forward vs underbust/ribcage. Negative = belly prominence.	--	scalar	derived	abdomen	enh	A	T,B
	knee_cm	Horizontal circumference at centre of kneecap. Bend knee slightly (~45 degrees).	5.3.22	circ	iso	leg	fit	B	B
	calf_cm	Maximum horizontal circumference of the calf. Stand with legs slightly apart.	5.3.24	circ	iso	leg	fit	B	B
	wrist_cm	Circumference at wrist, at prominent bone on little finger side (ulnar styloid).	5.3.19	circ	iso	arm	fit	D	T
	crotch_length_cm	Distance from front waist centre, through crotch, to back waist centre. Follow body surface.	5.4.18	length	iso	leg	fit	E	B
	front_rise_cm	Front waist to crotch point, along front body surface. Trouser front panel length.	--	length	tailor	leg	fit	E	B
	back_rise_cm	Back waist to crotch point, along back body surface. Trouser back panel length.	--	length	tailor	leg	fit	E	B
	shirt_length_cm	Side neck point down along front body contour to crotch level. Follow chest/stomach curve.	--	length	tailor	torso	fit	F	T
	back_neck_to_waist_cm	Cervicale (C7) down centre back along body contour to waist level. Tape follows spine curvature.	5.4.5	length	iso	torso	enh	H	T,D,O

Tier codes: core, std (standard), enh (enhanced), fit (fitted). Anny-only: underbust, mass, volume, bmi, body_fat, belly_depth.

Computation groups

Group	Measurements	Cost	Deps
A Core torso	height, bust, waist, hip, stomach, underbust, belly_depth	Cheap	--
B Limb sweeps	thigh, knee, calf, upperarm	Expensive	--
C Joint linear	shoulder_width, sleeve_length (ISO surface walk on re-posed body)	Very expensive	--
D Perpendicular	neck, wrist	Medium	--
E Mesh geometry	inseam (mesh sweep), crotch_length, front_rise, back_rise	Medium	--
F Surface trace	shirt_length	Medium	E
G Body composition	volume, mass, bmi, body_fat	Cheap	D
H Back length	back_neck_to_waist	Cheap	A

Group C and E have differentiable alternatives in measure_grad — use it for hot-loop optimization instead of calling measure() repeatedly.

Performance

measure() only runs the computation groups needed for the requested keys — use only= or preset= to skip expensive groups:

measure(body)                         # all groups — ~800 ms
measure(body, preset="core")          # group A only — ~100 ms
measure(body, only=["bust_cm"])       # group A only — ~100 ms
measure(body, only=["shoulder_width_cm"])  # groups A + C — ~200 ms

GPU acceleration

measure() accepts a device parameter (None = auto-detect CUDA):

measure(body, only=["bust_cm"], device="cuda")  # GPU forward pass
measure(body, device=None)                       # auto: CUDA if available

Optional extras

Extra	What it enables
[anny]	Anny body loader (requires torch)
[mhr]	MHR body loader (requires pymomentum)
[render]	4-view body renders (requires matplotlib, pyrender)

Without extras, only numpy, scipy, and trimesh are required.

Demo

Try the full pipeline at clad.you/size-aware/size-me.

Background

This library was built for Clad's size-aware virtual try-on pipeline. Read the full story: A 3D Body Scan for Nine Cents — Without SMPL.

License

Apache 2.0 — see LICENSE.