def _set_up(self):
self.v_shaped = self.shapedirs.dot(self.betas) + self.v_template
self.v_shaped_personal = self.v_shaped + self.v_personal
if sp.issparse(self.J_regressor):
self.J = sp_dot(self.J_regressor, self.v_shaped)
else:
self.J = ch.sum(self.J_regressor.T.reshape(-1, 1, 24) *
self.v_shaped.reshape(-1, 3, 1),
axis=0).T
self.v_posevariation = self.posedirs.dot(
posemap(self.bs_type)(self.pose))
self.v_poseshaped = self.v_shaped_personal + self.v_posevariation
self.A, A_global = self._global_rigid_transformation()
self.Jtr = ch.vstack([g[:3, 3] for g in A_global])
self.J_transformed = self.Jtr + self.trans.reshape((1, 3))
self.V = self.A.dot(self.weights.T)
rest_shape_h = ch.hstack(
(self.v_poseshaped, ch.ones((self.v_poseshaped.shape[0], 1))))
self.v_posed = ch.sum(self.V.T * rest_shape_h.reshape(-1, 4, 1),
axis=1)[:, :3]
self.v = self.v_posed + self.trans
def fit_consensus(frames, base_smpl, camera, frustum, model_data, nohands, icp_count, naked, display):
if nohands:
faces = faces_no_hands(base_smpl.f)
else:
faces = base_smpl.f
vis_rn_b = BoundaryRenderer(camera=camera, frustum=frustum, f=faces, num_channels=1)
vis_rn_m = ColoredRenderer(camera=camera, frustum=frustum, f=faces, vc=np.zeros_like(base_smpl), bgcolor=1,
num_channels=1)
model_template = Smpl(model_data)
model_template.betas[:] = base_smpl.betas.r
g_laplace = regularize_laplace()
g_model = regularize_model()
g_symmetry = regularize_symmetry()
face_ids = get_face_vertex_ids()
for step, (w_laplace, w_model, w_symmetry, sigma) in enumerate(zip(
np.linspace(6.5, 4.0, icp_count) if naked else np.linspace(4.0, 2.0, icp_count),
np.linspace(0.9, 0.6, icp_count) if naked else np.linspace(0.6, 0.3, icp_count),
np.linspace(3.6, 1.8, icp_count),
np.linspace(0.06, 0.003, icp_count),
)):
log.info('# Step {}'.format(step))
L = laplacian(model_template.r, base_smpl.f)
delta = L.dot(model_template.r)
w_laplace *= g_laplace.reshape(-1, 1)
w_model *= g_model.reshape(-1, 1)
w_symmetry *= g_symmetry.reshape(-1, 1)
E = {
'laplace': (sp_dot(L, base_smpl.v_shaped_personal) - delta) * w_laplace,
'model': (base_smpl.v_shaped_personal - model_template) * w_model,
'symmetry': (base_smpl.v_personal + np.array([1, -1, -1])
* base_smpl.v_personal[model_data['vert_sym_idxs']]) * w_symmetry,
}
log.info('## Matching rays with contours')
for current, f in enumerate(tqdm(frames)):
E['silh_{}'.format(current)] = ray_objective(f, sigma, base_smpl, camera, vis_rn_b, vis_rn_m)
#paper 2
E['face_{}'.format(current)] = ray_face(f, sigma, base_smpl, camera, face_ids)
log.info('## Run optimization')
ch.minimize(
E,
[base_smpl.v_personal, model_template.betas],
method='dogleg',
options={'maxiter': 15, 'e_3': 0.001},
callback=get_cb(frames[0], base_smpl, camera, frustum) if display else None
)
