neutralize mesh

Author: Soubhik Sanyal

requirements.txt

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+numpy
+scipy
+matplotlib
+scikit-image
+chumpy
+opencv-python
+absl-py
+tensorflow-gpu==1.12.0
+ipdb

util/using_flame_parameters.py

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+"""
+Author: Soubhik Sanyal
+Copyright (c) 2019, Soubhik Sanyal
+"""
+# This function Netralize the pose and expression of the predicted mesh and generates a template mesh with only the identity information
+import numpy as np
+import chumpy as ch
+from smpl_webuser.serialization import load_model
+from smpl_webuser.verts import verts_decorated
+from psbody.mesh import Mesh
+
+
+def make_prdicted_mesh_neutral(predicted_params_path, flame_model_path):
+    params = np.load(predicted_params_path, allow_pickle=True)
+    params = params[()]
+    pose = np.zeros(15)
+    expression = np.zeros(100)
+    shape = np.hstack((params['shape'], np.zeros(300-params['shape'].shape[0])))
+    flame_genral_model = load_model(flame_model_path)
+    generated_neutral_mesh = verts_decorated(ch.array([0.0,0.0,0.0]),
+                        ch.array(pose),
+                        ch.array(flame_genral_model.r),
+                        flame_genral_model.J_regressor,
+                        ch.array(flame_genral_model.weights),
+                        flame_genral_model.kintree_table,
+                        flame_genral_model.bs_style,
+                        flame_genral_model.f,
+                        bs_type=flame_genral_model.bs_type,
+                        posedirs=ch.array(flame_genral_model.posedirs),
+                        betas=ch.array(np.hstack((shape,expression))),#betas=ch.array(np.concatenate((theta[0,75:85], np.zeros(390)))), #
+                        shapedirs=ch.array(flame_genral_model.shapedirs),
+                        want_Jtr=True)
+    neutral_mesh = Mesh(v=generated_neutral_mesh.r, f=generated_neutral_mesh.f)
+    return neutral_mesh