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Direct 3D Body Measurement Estimation from Sparse Landmarks

This Github presents the code for the following paper: "Direct 3D Body Measurement Estimation from Sparse Landmarks" presented at VISAPP 2024.

TL;DR : Estimate 11 body measurements from 73 body landmarks with the accuracy of SOA methods.


🔨 Getting started

python -m venv lm2meas
source lm2meas/bin/activate
pip install -r requirements.txt

🏃 Running

python estimate_measurements.py --landmarks_path <path-to-landmarks>
                                --sex <subject-sex>
                                --scale <scale-landmarks-to-unit>
                                --normalize_viewpoint

where

  • --landmarks_path is the path to the landmarks file. See Landmarks for possible file extensions.
  • --sex is the subject sex: male or female. This decides the model to use.
  • --scale is the unit of measurement to scale to, the landmark coordinates are multiplied with the scale
  • --normalize_viewpoint is used to rotate the landmarks to have a similar viewpoint to the training data samples from CAESAR

⚠️ the landmarks need to be in millimeters. Check here ⚠️


🧍 Landmarks

The 73 landmarks are:

LANDMARKS_ORDER = ['10th Rib Midspine',
                    'Cervicale',
                    'Crotch',
                    'Lt. 10th Rib',
                    'Lt. ASIS',
                    'Lt. Acromion',
                    'Lt. Axilla, Ant.',
                    'Lt. Axilla, Post.',
                    'Lt. Calcaneous, Post.',
                    'Lt. Clavicale',
                    'Lt. Dactylion',
                    'Lt. Digit II',
                    'Lt. Femoral Lateral Epicn',
                    'Lt. Femoral Medial Epicn',
                    'Lt. Gonion',
                    'Lt. Humeral Lateral Epicn',
                    'Lt. Humeral Medial Epicn',
                    'Lt. Iliocristale',
                    'Lt. Infraorbitale',
                    'Lt. Knee Crease',
                    'Lt. Lateral Malleolus',
                    'Lt. Medial Malleolus',
                    'Lt. Metacarpal Phal. II',
                    'Lt. Metacarpal Phal. V',
                    'Lt. Metatarsal Phal. I',
                    'Lt. Metatarsal Phal. V',
                    'Lt. Olecranon',
                    'Lt. PSIS',
                    'Lt. Radial Styloid',
                    'Lt. Radiale',
                    'Lt. Sphyrion',
                    'Lt. Thelion/Bustpoint',
                    'Lt. Tragion',
                    'Lt. Trochanterion',
                    'Lt. Ulnar Styloid',
                    'Nuchale',
                    'Rt. 10th Rib',
                    'Rt. ASIS',
                    'Rt. Acromion',
                    'Rt. Axilla, Ant.',
                    'Rt. Axilla, Post.',
                    'Rt. Calcaneous, Post.',
                    'Rt. Clavicale',
                    'Rt. Dactylion',
                    'Rt. Digit II',
                    'Rt. Femoral Lateral Epicn',
                    'Rt. Femoral Medial Epicn',
                    'Rt. Gonion',
                    'Rt. Humeral Lateral Epicn',
                    'Rt. Humeral Medial Epicn',
                    'Rt. Iliocristale',
                    'Rt. Infraorbitale',
                    'Rt. Knee Crease',
                    'Rt. Lateral Malleolus',
                    'Rt. Medial Malleolus',
                    'Rt. Metacarpal Phal. II',
                    'Rt. Metacarpal Phal. V',
                    'Rt. Metatarsal Phal. I',
                    'Rt. Metatarsal Phal. V',
                    'Rt. Olecranon',
                    'Rt. PSIS',
                    'Rt. Radial Styloid',
                    'Rt. Radiale',
                    'Rt. Sphyrion',
                    'Rt. Thelion/Bustpoint',
                    'Rt. Tragion',
                    'Rt. Trochanterion',
                    'Rt. Ulnar Styloid',
                    'Sellion',
                    'Substernale',
                    'Supramenton',
                    'Suprasternale',
                    'Waist, Preferred, Post.']

The possible extensions of landmark_paths are:

  • .txt extension with each line "x y z landmark_name"
  • .json extension with {landmark_name: [x,y,z]}
  • .lnd extension (as defined in CAESAR where lines are "_ _ _ _ x y z landmark_name")

📏 Measurements

MEASUREMENTS_ORDER = ['Ankle Circumference (mm)',
                      'Arm Length (Shoulder to Elbow) (mm)',
                      'Arm Length (Shoulder to Wrist) (mm)',
                      'Arm Length (Spine to Wrist) (mm)',
                      'Chest Circumference (mm)',
                      'Crotch Height (mm)',
                      'Head Circumference (mm)',
                      'Hip Circ Max Height (mm)',
                      'Hip Circumference, Maximum (mm)',
                      'Neck Base Circumference (mm)',
                      'Stature (mm)']

⚖️ Scale

The input landmarks need to be defined in millimeter scale. The scale parameter multiplies the landmarks with the scale as landmarks * scale.

If you are unsure about the unit of the landmark coordinates, you can check the distance between landmark Sellion and Lt. Calcaneous, Post. (left heel). This should somewhat resemble to the height of the subject, from which you can infer the scale.


↗️ Viewpoint normalization

The models are trained on the CAESAR dataset so the viewpoint of the subject landmarks should be similar to the ones in the CAESAR dataset. To find the measurements from the landmarks from out-of-sample subjects (which can be in any position), we center the landmarks on the Substernale landmark and then minimize the landmark distances from the data/demo_landmarks.json landmarks, which has the same viewpoint as the examples from the CAESAR dataset.


💿 Demos

You can run the estimate_measurements.py with the demo data:

python estimate_measurements.py --landmarks_path data/demo_landmarks.json --sex male --scale 1000

Since the demo_landmarks.json are defined in meters, we use a scale of 1000.


📝 Notes

  • Landmarks need to be in millimeters
  • Measurements are in millimteres
  • Z-ax is the height of the person
  • Subject pose needs to be the A-pose
  • The model is trained on CAESAR dataset so the viewpoint of the subject landmarks should be similar to the ones in the CAESAR dataset

🏋🏻‍♂️ Training

We provide the names of the subjects used for training and testing the models in data/train_test_names.npz to facilitate comparability and repeatability.

data = np.load("data/train_test_names.npz")
names_train_male = data["names_train_male"]
names_test_male = data["names_test_male"]
names_train_female = data["names_train_female"]
names_test_female = data["names_test_female"]

Citation

If you use our work, please reference our paper:

@conference{Bojanic-VISAPP24,
  author={David Bojani{\'c}. and Kristijan Bartol. and Tomislav Petkovi{\'c}. and Tomislav Pribani{\'c}.},
  title={Direct 3D Body Measurement Estimation from Sparse Landmarks},
  booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
  year={2024},
  pages={524-531},
  publisher={SciTePress},
  organization={INSTICC},
  doi={10.5220/0012384000003660},
  isbn={978-989-758-679-8},
  issn={2184-4321},
}