This repository contains the code to easy use SMPL with TensorFlow.
$ pip install git+https://github.com/opendeeple/tf-smpl.git
- Sign in into https://smpl.is.tue.mpg.de
- Download SMPL version 1.0.0 for Python 2.7 (10 shape PCs)
- Extract SMPL_python_v.1.0.0.zip
$ smpl --config configs/example.conf --motion motions\*.npz
import tensorflow as tf
from tf_smpl import SMPL
smpl = SMPL("<smpl_model_extraced_folder>/basicModel_f_lbs_10_207_0_v1.0.0.pkl")
# calculate SMPL vertices
batch_size = 16
v_body = smpl(
shapes=tf.zeros(shape=[batch_size, 10]), # sample shapes (betas)
poses=tf.zeros(shape=[batch_size, 72]), # sample poses
trans=tf.zeros(shape=[batch_size, 3]) # sample trans
)
# calculate SMPL vertices for sequences
sequence_size = 3
v_body = smpl(
shapes=tf.zeros(shape=[batch_size, sequence_size, 10]), # sample shapes (betas)
poses=tf.zeros(shape=[batch_size, sequence_size, 72]), # sample poses
trans=tf.zeros(shape=[batch_size, sequence_size, 3]) # sample trans
)Calculate SMPL vertices with get middle variables
List of midle variables
- v_shaped
- v_posed
- J_rotations
- J_locations
- J_transforms
v_body, body_dict = smpl(
...,
includes=["v_shaped", "J_locations"] # for get middle variables
)
print(body_dict) # display body_dict:middle variablesCalculate SMPL normals
v_normals = smpl.normals(v_body)Calculate neighbours of SMPL with Outfit
neighbours = smpl.neighbours(v_body, v_outfit)
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