Dimension Bundles | DimensionsPot

Request a focused subset of dimensions using predefined bundles, or cherry-pick individual measurements.

Available bundles

Bundle	Dimensions	Key measurements
`FULL_BODY`	130	All available dimensions
`HEAD_FACE`	20	IPD, head breadth, face length, bridge width, bitragion arcs, neck circumference
`HAND_ARM`	32	All digit lengths & widths, wrist, forearm, arm length, span, reach
`TORSO`	29	Chest, waist, hip, shoulder breadth, sitting heights, bust/underbust
`LEGS_FEET`	34	Full foot geometry, calf, knee, inseam, ankle, trochanterion

Set the bundle in output_settings.requested_dimensions:

"requested_dimensions": {
  "bundle": "HEAD_FACE"
}

Cherry-picking specific dimensions

Use specific_dimensions to request any subset across bundles in a single call:

"requested_dimensions": {
  "specific_dimensions": [
    "interpupillary_distance",
    "head_breadth",
    "chest_circumference",
    "inseam_length"
  ]
}

Mixing dimensions from different bundles works. If specific_dimensions is an empty array, all dimensions are returned (same as FULL_BODY).

Filtering by confidence

Use confidence_score_threshold to automatically exclude dimensions below a reliability threshold:

"output_format": {
  "confidence_score_threshold": 75
}

Dimensions with confidence_score < 75 are omitted from the response. This is useful when you only want to act on high-reliability predictions.

Optional response fields

Control payload size with two boolean flags:

Flag	Default	What it adds
`include_range_95`	`false`	Adds `range_95: [lower, upper]` to each dimension
`include_iso_codes`	`false`	Adds `iso_code` (ISO 7250-1:2017 code) to each dimension

"output_format": {
  "include_range_95": true,
  "include_iso_codes": true
}