def __init__(self, args, joint_topology, origin_offsets: torch.Tensor,
device, characters):
self.args = args
self.joint_topology = joint_topology
self.edges = build_edge_topology(joint_topology,
torch.zeros((len(joint_topology), 3)))
self.fk = ForwardKinematics(args, self.edges)
self.height = [] # for normalize ee_loss
self.real_height = []
for char in characters:
if args.use_sep_ee:
h = BVH_file(get_std_bvh(dataset=char)).get_ee_length()
else:
h = BVH_file(get_std_bvh(dataset=char)).get_height()
if args.ee_loss_fact == 'learn':
h = torch.tensor(h, dtype=torch.float)
else:
h = torch.tensor(h, dtype=torch.float, requires_grad=False)
self.real_height.append(
BVH_file(get_std_bvh(dataset=char)).get_height())
self.height.append(h.unsqueeze(0))
self.real_height = torch.tensor(self.real_height, device=device)
self.height = torch.cat(self.height, dim=0)
self.height = self.height.to(device)
if not args.use_sep_ee: self.height.unsqueeze_(-1)
if args.ee_loss_fact == 'learn': self.height_para = [self.height]
else: self.height_para = []
if not args.simple_operator:
self.auto_encoder = AE(args, topology=self.edges).to(device)
self.discriminator = Discriminator(args, self.edges).to(device)
self.static_encoder = StaticEncoder(args, self.edges).to(device)
else:
raise Exception('Conventional operator not yet implemented')
def __init__(self, file_path=None, args=None, dataset=None, new_root=None):
if file_path is None:
file_path = get_std_bvh(dataset=dataset)
self.anim, self._names, self.frametime = BVH.load(file_path)
if new_root is not None:
self.set_new_root(new_root)
self.skeleton_type = -1
self.edges = []
self.edge_mat = []
self.edge_num = 0
self._topology = None
self.ee_length = []
for i, name in enumerate(self._names):
if ':' in name:
name = name[name.find(':') + 1:]
self._names[i] = name
full_fill = [1] * len(corps_names)
for i, ref_names in enumerate(corps_names):
for ref_name in ref_names:
if ref_name not in self._names:
full_fill[i] = 0
break
if full_fill[3]:
self.skeleton_type = 3
else:
for i, _ in enumerate(full_fill):
if full_fill[i]:
self.skeleton_type = i
break
if self.skeleton_type == 2 and full_fill[4]:
self.skeleton_type = 4
if 'Neck1' in self._names:
self.skeleton_type = 5
if 'Left_End' in self._names:
self.skeleton_type = 6
if 'Three_Arms_Hips' in self._names:
self.skeleton_type = 7
if 'Three_Arms_Hips_split' in self._names:
self.skeleton_type = 8
if 'LHipJoint' in self._names:
self.skeleton_type = 3
if 'HipsPrisoner' in self._names:
self.skeleton_type = 9
if 'Spine1_split' in self._names:
self.skeleton_type = 10
"""
4.
Here, you need to assign self.skeleton_type the corresponding index of your own dataset in corps_names or ee_names list.
You can use self._names, which contains the joints name in original bvh file, to write your own if statement.
"""
# if ...:
# self.skeleton_type = 11
if self.skeleton_type == -1:
print(self._names)
raise Exception('Unknown skeleton')
if self.skeleton_type == 0:
self.set_new_root(1)
self.details = []
for i, name in enumerate(self._names):
if ':' in name: name = name[name.find(':') + 1:]
if name not in corps_names[self.skeleton_type]:
self.details.append(i)
self.joint_num = self.anim.shape[1]
self.corps = []
self.simplified_name = []
self.simplify_map = {}
self.inverse_simplify_map = {}
for name in corps_names[self.skeleton_type]:
for j in range(self.anim.shape[1]):
if name == self._names[j]:
self.corps.append(j)
break
if len(self.corps) != len(corps_names[self.skeleton_type]):
for i in self.corps:
print(self._names[i], end=' ')
print(self.corps, self.skeleton_type, len(self.corps), sep='\n')
raise Exception('Problem in file', file_path)
self.ee_id = []
for i in ee_names[self.skeleton_type]:
self.ee_id.append(corps_names[self.skeleton_type].index(i))
self.joint_num_simplify = len(self.corps)
for i, j in enumerate(self.corps):
self.simplify_map[j] = i
self.inverse_simplify_map[i] = j
self.simplified_name.append(self._names[j])
self.inverse_simplify_map[0] = -1
for i in range(self.anim.shape[1]):
if i in self.details:
self.simplify_map[i] = -1
self.edges = build_edge_topology(self.topology, self.offset)