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rignetconnect.py
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rignetconnect.py
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import os
import numpy as np
import itertools as it
import torch
from torch_geometric.data import Data
from torch_geometric.utils import add_self_loops
from .RigNet.utils.rig_parser import Info
from .RigNet.utils.tree_utils import TreeNode
from .RigNet.utils.cluster_utils import meanshift_cluster, nms_meanshift
from .RigNet.utils.mst_utils import increase_cost_for_outside_bone, primMST_symmetry, loadSkel_recur, inside_check, flip
from .RigNet.utils.mst_utils import sample_on_bone
from .RigNet.models.GCN import JOINTNET_MASKNET_MEANSHIFT as JOINTNET
from .RigNet.models.ROOT_GCN import ROOTNET
from .RigNet.models.PairCls_GCN import PairCls as BONENET
from .RigNet.models.SKINNING import SKINNET
import bpy
from mathutils import Matrix
from .ob_utils import sampling as mesh_sampling
from .ob_utils.geometry import get_tpl_edges
from .ob_utils.geometry import get_geo_edges
from .ob_utils.geometry import NormalizedMeshData
from .ob_utils.objects import ArmatureGenerator
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class MeshStorage:
"""Store Mesh Data and samples"""
_instance = None # stores singleton instance
_mesh_data = None
_mesh_sampler = None
_surf_geodesic = None
_voxels = None
def __init__(self, samples=2000):
self._samples = samples
def set_mesh_data(self, mesh_obj):
self._mesh_data = NormalizedMeshData(mesh_obj)
@property
def mesh_data(self):
assert self._mesh_data is not None
return self._mesh_data
@property
def surface_geodesic(self):
if self._surf_geodesic is None:
assert self._mesh_data is not None
self._surf_geodesic = self.mesh_sampler.calc_geodesic(samples=self._samples)
return self._surf_geodesic
@property
def voxels(self):
if self._voxels is None:
assert self._mesh_data is not None
self._voxels = self._mesh_data.voxels()
return self._voxels
@property
def mesh_sampler(self):
if self._mesh_sampler is None:
assert self._mesh_data is not None
self._mesh_sampler = mesh_sampling.MeshSampler(self._mesh_data.mesh_f, self._mesh_data.mesh_v,
self._mesh_data.mesh_vn, self._mesh_data.tri_areas)
return self._mesh_sampler
def getInitId(data, model):
"""
predict root joint ID via rootnet
:param data:
:param model:
:return:
"""
with torch.no_grad():
root_prob, _ = model(data, shuffle=False)
root_prob = torch.sigmoid(root_prob).data.cpu().numpy()
root_id = np.argmax(root_prob)
return root_id
def create_single_data(mesh_storage: MeshStorage):
"""
create input data for the network. The data is wrapped by Data structure in pytorch-geometric library
"""
mesh_data = mesh_storage.mesh_data
# vertices
v = np.concatenate((mesh_data.mesh_v, mesh_data.mesh_vn), axis=1)
v = torch.from_numpy(v).float()
# topology edges
print(" gathering topological edges.")
tpl_e = get_tpl_edges(mesh_data.mesh_v, mesh_data.mesh_f).T
tpl_e = torch.from_numpy(tpl_e).long()
tpl_e, _ = add_self_loops(tpl_e, num_nodes=v.size(0))
# surface geodesic distance matrix
print(" calculating surface geodesic matrix.")
surface_geodesic = mesh_storage.surface_geodesic
# geodesic edges
print(" gathering geodesic edges.")
geo_e = get_geo_edges(surface_geodesic, mesh_data.mesh_v).T
geo_e = torch.from_numpy(geo_e).long()
geo_e, _ = add_self_loops(geo_e, num_nodes=v.size(0))
# batch
batch = torch.zeros(len(v), dtype=torch.long)
geo_data = Data(x=v[:, 3:6], pos=v[:, 0:3], tpl_edge_index=tpl_e, geo_edge_index=geo_e, batch=batch)
return geo_data
def add_joints_data(input_data, vox, joint_pred_net, threshold, bandwidth=None, mesh_filename=None):
"""
Predict joints
:param input_data: wrapped input data
:param vox: voxelized mesh
:param joint_pred_net: network for predicting joints
:param threshold: density threshold to filter out shifted points
:param bandwidth: bandwidth for meanshift clustering
:param mesh_filename: mesh filename for visualization
:return: wrapped data with predicted joints, pair-wise bone representation added.
"""
data_displacement, _, attn_pred, bandwidth_pred = joint_pred_net(input_data)
y_pred = data_displacement + input_data.pos
y_pred_np = y_pred.data.cpu().numpy()
attn_pred_np = attn_pred.data.cpu().numpy()
y_pred_np, index_inside = inside_check(y_pred_np, vox)
attn_pred_np = attn_pred_np[index_inside, :]
y_pred_np = y_pred_np[attn_pred_np.squeeze() > 1e-3]
attn_pred_np = attn_pred_np[attn_pred_np.squeeze() > 1e-3]
# symmetrize points by reflecting
y_pred_np_reflect = y_pred_np * np.array([[-1, 1, 1]])
y_pred_np = np.concatenate((y_pred_np, y_pred_np_reflect), axis=0)
attn_pred_np = np.tile(attn_pred_np, (2, 1))
if not bandwidth:
bandwidth = bandwidth_pred.item()
y_pred_np = meanshift_cluster(y_pred_np, bandwidth, attn_pred_np, max_iter=40)
Y_dist = np.sum(((y_pred_np[np.newaxis, ...] - y_pred_np[:, np.newaxis, :]) ** 2), axis=2)
density = np.maximum(bandwidth ** 2 - Y_dist, np.zeros(Y_dist.shape))
density = np.sum(density, axis=0)
density_sum = np.sum(density)
y_pred_np = y_pred_np[density / density_sum > threshold]
density = density[density / density_sum > threshold]
pred_joints = nms_meanshift(y_pred_np, density, bandwidth)
pred_joints, _ = flip(pred_joints)
# prepare and add new data members
pairs = list(it.combinations(range(pred_joints.shape[0]), 2))
pair_attr = []
for pr in pairs:
dist = np.linalg.norm(pred_joints[pr[0]] - pred_joints[pr[1]])
bone_samples = sample_on_bone(pred_joints[pr[0]], pred_joints[pr[1]])
bone_samples_inside, _ = inside_check(bone_samples, vox)
outside_proportion = len(bone_samples_inside) / (len(bone_samples) + 1e-10)
attr = np.array([dist, outside_proportion, 1])
pair_attr.append(attr)
pairs = np.array(pairs)
pair_attr = np.array(pair_attr)
pairs = torch.from_numpy(pairs).float()
pair_attr = torch.from_numpy(pair_attr).float()
pred_joints = torch.from_numpy(pred_joints).float()
joints_batch = torch.zeros(len(pred_joints), dtype=torch.long)
pairs_batch = torch.zeros(len(pairs), dtype=torch.long)
input_data.joints = pred_joints
input_data.pairs = pairs
input_data.pair_attr = pair_attr
input_data.joints_batch = joints_batch
input_data.pairs_batch = pairs_batch
return input_data
def predict_skeleton(input_data, vox, root_pred_net, bone_pred_net):
"""
Predict skeleton structure based on joints
:param input_data: wrapped data
:param vox: voxelized mesh
:param root_pred_net: network to predict root
:param bone_pred_net: network to predict pairwise connectivity cost
:return: predicted skeleton structure
"""
root_id = getInitId(input_data, root_pred_net)
pred_joints = input_data.joints.data.cpu().numpy()
with torch.no_grad():
connect_prob, _ = bone_pred_net(input_data, permute_joints=False)
connect_prob = torch.sigmoid(connect_prob)
pair_idx = input_data.pairs.long().data.cpu().numpy()
prob_matrix = np.zeros((len(input_data.joints), len(input_data.joints)))
prob_matrix[pair_idx[:, 0], pair_idx[:, 1]] = connect_prob.data.cpu().numpy().squeeze()
prob_matrix = prob_matrix + prob_matrix.transpose()
cost_matrix = -np.log(prob_matrix + 1e-10)
cost_matrix = increase_cost_for_outside_bone(cost_matrix, pred_joints, vox)
pred_skel = Info()
parent, key, _ = primMST_symmetry(cost_matrix, root_id, pred_joints)
for i in range(len(parent)):
if parent[i] == -1:
pred_skel.root = TreeNode('root', tuple(pred_joints[i]))
break
loadSkel_recur(pred_skel.root, i, None, pred_joints, parent)
pred_skel.joint_pos = pred_skel.get_joint_dict()
return pred_skel
def pts2line(pts, lines):
'''
Calculate points-to-bone distance. Point to line segment distance refer to
https://stackoverflow.com/questions/849211/shortest-distance-between-a-point-and-a-line-segment
:param pts: N*3
:param lines: N*6, where [N,0:3] is the starting position and [N, 3:6] is the ending position
:return: origins are the neatest projected position of the point on the line.
ends are the points themselves.
dist is the distance in between, which is the distance from points to lines.
Origins and ends will be used for generate rays.
'''
l2 = np.sum((lines[:, 3:6] - lines[:, 0:3]) ** 2, axis=1)
origins = np.zeros((len(pts) * len(lines), 3))
ends = np.zeros((len(pts) * len(lines), 3))
dist = np.zeros((len(pts) * len(lines)))
for l in range(len(lines)):
if np.abs(l2[l]) < 1e-8: # for zero-length edges
origins[l * len(pts):(l + 1) * len(pts)] = lines[l][0:3]
else: # for other edges
t = np.sum((pts - lines[l][0:3][np.newaxis, :]) * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :], axis=1) / \
l2[l]
t = np.clip(t, 0, 1)
t_pos = lines[l][0:3][np.newaxis, :] + t[:, np.newaxis] * (lines[l][3:6] - lines[l][0:3])[np.newaxis, :]
origins[l * len(pts):(l + 1) * len(pts)] = t_pos
ends[l * len(pts):(l + 1) * len(pts)] = pts
dist[l * len(pts):(l + 1) * len(pts)] = np.linalg.norm(
origins[l * len(pts):(l + 1) * len(pts)] - ends[l * len(pts):(l + 1) * len(pts)], axis=1)
return origins, ends, dist
def calc_pts2bone_visible_mat(bvhtree, origins, ends):
'''
Check whether the surface point is visible by the internal bone.
Visible is defined as no occlusion on the path between.
:param mesh:
:param surface_pts: points on the surface (n*3)
:param origins: origins of rays
:param ends: ends of the rays, together with origins, we can decide the direction of the ray.
:return: binary visibility matrix (n*m), where 1 indicate the n-th surface point is visible to the m-th ray
'''
ray_dirs = ends - origins
min_hit_distance = []
for ray_dir, origin in zip(ray_dirs, origins):
# FIXME: perhaps we should sample more distances
location, normal, index, distance = bvhtree.ray_cast(origin, ray_dir + 1e-15)
if location:
min_hit_distance.append(np.linalg.norm(np.array(location) - origin))
else:
min_hit_distance.append(np.linalg.norm(ray_dir))
min_hit_distance = np.array(min_hit_distance)
distance = np.linalg.norm(ray_dirs, axis=1)
vis_mat = (np.abs(min_hit_distance - distance) < 1e-4)
return vis_mat
def calc_geodesic_matrix(bones, mesh_v, surface_geodesic, bvh_tree, use_sampling=False):
"""
calculate volumetric geodesic distance from vertices to each bones
:param bones: B*6 numpy array where each row stores the starting and ending joint position of a bone
:param mesh_v: V*3 mesh vertices
:param surface_geodesic: geodesic distance matrix of all vertices
:return: an approaximate volumetric geodesic distance matrix V*B, were (v,b) is the distance from vertex v to bone b
"""
if use_sampling:
# TODO: perhaps not required with blender's bvh tree
# will have to decimate the mesh otherwise
# also, this should rather be done outside the function
subsamples = mesh_v
else:
subsamples = mesh_v
origins, ends, pts_bone_dist = pts2line(subsamples, bones)
pts_bone_visibility = calc_pts2bone_visible_mat(bvh_tree, origins, ends)
pts_bone_visibility = pts_bone_visibility.reshape(len(bones), len(subsamples)).transpose()
pts_bone_dist = pts_bone_dist.reshape(len(bones), len(subsamples)).transpose()
# remove visible points which are too far
for b in range(pts_bone_visibility.shape[1]):
visible_pts = np.argwhere(pts_bone_visibility[:, b] == 1).squeeze(1)
if len(visible_pts) == 0:
continue
threshold_b = np.percentile(pts_bone_dist[visible_pts, b], 15)
pts_bone_visibility[pts_bone_dist[:, b] > 1.3 * threshold_b, b] = False
visible_matrix = np.zeros(pts_bone_visibility.shape)
visible_matrix[np.where(pts_bone_visibility == 1)] = pts_bone_dist[np.where(pts_bone_visibility == 1)]
for c in range(visible_matrix.shape[1]):
unvisible_pts = np.argwhere(pts_bone_visibility[:, c] == 0).squeeze(1)
visible_pts = np.argwhere(pts_bone_visibility[:, c] == 1).squeeze(1)
if len(visible_pts) == 0:
visible_matrix[:, c] = pts_bone_dist[:, c]
continue
for r in unvisible_pts:
dist1 = np.min(surface_geodesic[r, visible_pts])
nn_visible = visible_pts[np.argmin(surface_geodesic[r, visible_pts])]
if np.isinf(dist1):
visible_matrix[r, c] = 8.0 + pts_bone_dist[r, c]
else:
visible_matrix[r, c] = dist1 + visible_matrix[nn_visible, c]
if use_sampling:
nn_dist = np.sum((mesh_v[:, np.newaxis, :] - subsamples[np.newaxis, ...]) ** 2, axis=2)
nn_ind = np.argmin(nn_dist, axis=1)
visible_matrix = visible_matrix[nn_ind, :]
return visible_matrix
def add_duplicate_joints(skel):
this_level = [skel.root]
while this_level:
next_level = []
for p_node in this_level:
if len(p_node.children) > 1:
new_children = []
for dup_id in range(len(p_node.children)):
p_node_new = TreeNode(p_node.name + '_dup_{:d}'.format(dup_id), p_node.pos)
p_node_new.overlap=True
p_node_new.parent = p_node
p_node_new.children = [p_node.children[dup_id]]
# for user interaction, we move overlapping joints a bit to its children
p_node_new.pos = np.array(p_node_new.pos) + 0.03 * np.linalg.norm(np.array(p_node.children[dup_id].pos) - np.array(p_node_new.pos))
p_node_new.pos = (p_node_new.pos[0], p_node_new.pos[1], p_node_new.pos[2])
p_node.children[dup_id].parent = p_node_new
new_children.append(p_node_new)
p_node.children = new_children
p_node.overlap = False
next_level += p_node.children
this_level = next_level
return skel
def mapping_bone_index(bones_old, bones_new):
bone_map = {}
for i in range(len(bones_old)):
bone_old = bones_old[i][np.newaxis, :]
dist = np.linalg.norm(bones_new - bone_old, axis=1)
ni = np.argmin(dist)
bone_map[i] = ni
return bone_map
def get_bones(skel):
"""
extract bones from skeleton struction
:param skel: input skeleton
:return: bones are B*6 array where each row consists starting and ending points of a bone
bone_name are a list of B elements, where each element consists starting and ending joint name
leaf_bones indicate if this bone is a virtual "leaf" bone.
We add virtual "leaf" bones to the leaf joints since they always have skinning weights as well
"""
bones = []
bone_name = []
leaf_bones = []
this_level = [skel.root]
while this_level:
next_level = []
for p_node in this_level:
p_pos = np.array(p_node.pos)
next_level += p_node.children
for c_node in p_node.children:
c_pos = np.array(c_node.pos)
bones.append(np.concatenate((p_pos, c_pos))[np.newaxis, :])
bone_name.append([p_node.name, c_node.name])
leaf_bones.append(False)
if len(c_node.children) == 0:
bones.append(np.concatenate((c_pos, c_pos))[np.newaxis, :])
bone_name.append([c_node.name, c_node.name+'_leaf'])
leaf_bones.append(True)
this_level = next_level
bones = np.concatenate(bones, axis=0)
return bones, bone_name, leaf_bones
def assemble_skel_skin(skel, attachment):
bones_old, bone_names_old, _ = get_bones(skel)
skel_new = add_duplicate_joints(skel)
bones_new, bone_names_new, _ = get_bones(skel_new)
bone_map = mapping_bone_index(bones_old, bones_new)
skel_new.joint_pos = skel_new.get_joint_dict()
skel_new.joint_skin = []
for v in range(len(attachment)):
vi_skin = [str(v)]
skw = attachment[v]
skw = skw / (np.sum(skw) + 1e-10)
for i in range(len(skw)):
if i == len(bones_old):
break
if skw[i] > 1e-5:
bind_joint_name = bone_names_new[bone_map[i]][0]
bind_weight = skw[i]
vi_skin.append(bind_joint_name)
vi_skin.append(str(bind_weight))
skel_new.joint_skin.append(vi_skin)
return skel_new
def post_filter(skin_weights, topology_edge, num_ring=1):
skin_weights_new = np.zeros_like(skin_weights)
for v in range(len(skin_weights)):
adj_verts_multi_ring = []
current_seeds = [v]
for r in range(num_ring):
adj_verts = []
for seed in current_seeds:
adj_edges = topology_edge[:, np.argwhere(topology_edge == seed)[:, 1]]
adj_verts_seed = list(set(adj_edges.flatten().tolist()))
adj_verts_seed.remove(seed)
adj_verts += adj_verts_seed
adj_verts_multi_ring += adj_verts
current_seeds = adj_verts
adj_verts_multi_ring = list(set(adj_verts_multi_ring))
if v in adj_verts_multi_ring:
adj_verts_multi_ring.remove(v)
skin_weights_neighbor = [skin_weights[int(i), :][np.newaxis, :] for i in adj_verts_multi_ring]
skin_weights_neighbor = np.concatenate(skin_weights_neighbor, axis=0)
#max_bone_id = np.argmax(skin_weights[v, :])
#if np.sum(skin_weights_neighbor[:, max_bone_id]) < 0.17 * len(skin_weights_neighbor):
# skin_weights_new[v, :] = np.mean(skin_weights_neighbor, axis=0)
#else:
# skin_weights_new[v, :] = skin_weights[v, :]
skin_weights_new[v, :] = np.mean(skin_weights_neighbor, axis=0)
#skin_weights_new[skin_weights_new.sum(axis=1) == 0, :] = skin_weights[skin_weights_new.sum(axis=1) == 0, :]
return skin_weights_new
def predict_skinning(input_data, pred_skel, skin_pred_net, surface_geodesic, bvh_tree):
"""
predict skinning
:param input_data: wrapped input data
:param pred_skel: predicted skeleton
:param skin_pred_net: network to predict skinning weights
:param surface_geodesic: geodesic distance matrix of all vertices
:param mesh_filename: mesh filename
:return: predicted rig with skinning weights information
"""
global DEVICE, output_folder
num_nearest_bone = 5
bones, bone_names, bone_isleaf = get_bones(pred_skel)
mesh_v = input_data.pos.data.cpu().numpy()
print(" calculating volumetric geodesic distance from vertices to bone. This step takes some time...")
geo_dist = calc_geodesic_matrix(bones, mesh_v, surface_geodesic, bvh_tree)
input_samples = [] # joint_pos (x, y, z), (bone_id, 1/D)*5
loss_mask = []
skin_nn = []
for v_id in range(len(mesh_v)):
geo_dist_v = geo_dist[v_id]
bone_id_near_to_far = np.argsort(geo_dist_v)
this_sample = []
this_nn = []
this_mask = []
for i in range(num_nearest_bone):
if i >= len(bones):
this_sample += bones[bone_id_near_to_far[0]].tolist()
this_sample.append(1.0 / (geo_dist_v[bone_id_near_to_far[0]] + 1e-10))
this_sample.append(bone_isleaf[bone_id_near_to_far[0]])
this_nn.append(0)
this_mask.append(0)
else:
skel_bone_id = bone_id_near_to_far[i]
this_sample += bones[skel_bone_id].tolist()
this_sample.append(1.0 / (geo_dist_v[skel_bone_id] + 1e-10))
this_sample.append(bone_isleaf[skel_bone_id])
this_nn.append(skel_bone_id)
this_mask.append(1)
input_samples.append(np.array(this_sample)[np.newaxis, :])
skin_nn.append(np.array(this_nn)[np.newaxis, :])
loss_mask.append(np.array(this_mask)[np.newaxis, :])
skin_input = np.concatenate(input_samples, axis=0)
loss_mask = np.concatenate(loss_mask, axis=0)
skin_nn = np.concatenate(skin_nn, axis=0)
skin_input = torch.from_numpy(skin_input).float()
input_data.skin_input = skin_input
input_data.to(DEVICE)
skin_pred = skin_pred_net(input_data)
skin_pred = torch.softmax(skin_pred, dim=1)
skin_pred = skin_pred.data.cpu().numpy()
skin_pred = skin_pred * loss_mask
skin_nn = skin_nn[:, 0:num_nearest_bone]
skin_pred_full = np.zeros((len(skin_pred), len(bone_names)))
for v in range(len(skin_pred)):
for nn_id in range(len(skin_nn[v, :])):
skin_pred_full[v, skin_nn[v, nn_id]] = skin_pred[v, nn_id]
print(" filtering skinning prediction")
tpl_e = input_data.tpl_edge_index.data.cpu().numpy()
skin_pred_full = post_filter(skin_pred_full, tpl_e, num_ring=1)
skin_pred_full[skin_pred_full < np.max(skin_pred_full, axis=1, keepdims=True) * 0.35] = 0.0
skin_pred_full = skin_pred_full / (skin_pred_full.sum(axis=1, keepdims=True) + 1e-10)
skel_res = assemble_skel_skin(pred_skel, skin_pred_full)
return skel_res
class Networks:
def __init__(self, model_dir="", load_networks=True, load_skinning=True):
self.joint_net = None
self.root_net = None
self.bone_net = None
self.skin_net = None
self._load_skinning = load_skinning
self.model_dir = model_dir if model_dir else bpy.context.preferences.addons[__package__].preferences.model_path
if load_networks:
self.load_networks()
def load_networks(self):
print("loading all networks...")
joint_net = JOINTNET()
joint_net.to(DEVICE)
joint_net.eval()
joint_net_checkpoint = torch.load(os.path.join(self.model_dir, 'gcn_meanshift/model_best.pth.tar'))
joint_net.load_state_dict(joint_net_checkpoint['state_dict'])
self.joint_net = joint_net
print(" joint prediction network loaded.")
root_net = ROOTNET()
root_net.to(DEVICE)
root_net.eval()
root_net_checkpoint = torch.load(os.path.join(self.model_dir, 'rootnet/model_best.pth.tar'))
root_net.load_state_dict(root_net_checkpoint['state_dict'])
self.root_net = root_net
print(" root prediction network loaded.")
bone_net = BONENET()
bone_net.to(DEVICE)
bone_net.eval()
bone_net_checkpoint = torch.load(os.path.join(self.model_dir, 'bonenet/model_best.pth.tar'))
bone_net.load_state_dict(bone_net_checkpoint['state_dict'])
self.bone_net = bone_net
print(" connection prediction network loaded.")
if self._load_skinning:
skin_net = SKINNET(nearest_bone=5, use_Dg=True, use_Lf=True)
skin_net_checkpoint = torch.load(os.path.join(self.model_dir, 'skinnet/model_best.pth.tar'))
skin_net.load_state_dict(skin_net_checkpoint['state_dict'])
skin_net.to(DEVICE)
skin_net.eval()
self.skin_net = skin_net
print(" skinning prediction network loaded.")
def init_data(mesh_obj, samples=2000):
mesh_storage = MeshStorage(samples)
mesh_storage.set_mesh_data(mesh_obj)
predict_data = create_single_data(mesh_storage)
predict_data.to(DEVICE)
return predict_data, mesh_storage
def predict_joint(predict_data, joint_network, mesh_storage: MeshStorage, bandwidth, threshold):
print("predicting joints")
predict_data = add_joints_data(predict_data, mesh_storage.voxels, joint_network, threshold, bandwidth=bandwidth)
predict_data.to(DEVICE)
return predict_data
def predict_hierarchy(predict_data, networks: Networks, mesh_storage: MeshStorage):
print("predicting connectivity")
predicted_skeleton = predict_skeleton(predict_data, mesh_storage.voxels, networks.root_net, networks.bone_net)
return predicted_skeleton
def predict_weights(predict_data, predicted_skeleton, skin_network, mesh_storage: MeshStorage):
print("predicting skinning")
mesh_data = mesh_storage.mesh_data
bvh_tree = mesh_data.bvh_tree
predicted_rig = predict_skinning(predict_data, predicted_skeleton, skin_network, mesh_storage.surface_geodesic, bvh_tree)
# here we reverse the normalization to the original scale and position
predicted_rig.normalize(mesh_data.scale_normalize, -mesh_data.translation_normalize)
return predicted_rig
def create_armature(mesh_obj, predicted_rig):
mesh_obj.vertex_groups.clear()
for obj in bpy.data.objects:
obj.select_set(False)
mat = Matrix(((1.0, 0.0, 0.0, 0.0),
(0.0, 0.0, -1.0, 0.0),
(0.0, 1.0, 0.0, 0.0),
(0.0, 0.0, 0.0, 1.0)))
new_arm = ArmatureGenerator(predicted_rig, mesh_obj).generate(matrix=mat)
torch.cuda.empty_cache()
return new_arm
def clear():
torch.cuda.empty_cache()