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'''
Adjusted version of other PyTorch implementation of the SMAL/SMPL model
see:
1.) https://github.com/silviazuffi/smalst/blob/master/smal_model/smal_torch.py
2.) https://github.com/benjiebob/SMALify/blob/master/smal_model/smal_torch.py
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import numpy as np
def batch_skew(vec, batch_size=None):
"""
vec is N x 3, batch_size is int
returns N x 3 x 3. Skew_sym version of each matrix.
"""
device = vec.device
if batch_size is None:
batch_size = vec.shape.as_list()[0]
col_inds = torch.LongTensor([1, 2, 3, 5, 6, 7])
indices = torch.reshape(torch.reshape(torch.arange(0, batch_size) * 9, [-1, 1]) + col_inds, [-1, 1])
updates = torch.reshape(
torch.stack(
[
-vec[:, 2], vec[:, 1], vec[:, 2], -vec[:, 0], -vec[:, 1],
vec[:, 0]
],
dim=1), [-1])
out_shape = [batch_size * 9]
res = torch.Tensor(np.zeros(out_shape[0])).to(device=device)
res[np.array(indices.flatten())] = updates
res = torch.reshape(res, [batch_size, 3, 3])
return res
def batch_rodrigues(theta):
"""
Theta is Nx3
"""
device = theta.device
batch_size = theta.shape[0]
angle = (torch.norm(theta + 1e-8, p=2, dim=1)).unsqueeze(-1)
r = (torch.div(theta, angle)).unsqueeze(-1)
angle = angle.unsqueeze(-1)
cos = torch.cos(angle)
sin = torch.sin(angle)
outer = torch.matmul(r, r.transpose(1,2))
eyes = torch.eye(3).unsqueeze(0).repeat([batch_size, 1, 1]).to(device=device)
H = batch_skew(r, batch_size=batch_size)
R = cos * eyes + (1 - cos) * outer + sin * H
return R
def batch_lrotmin(theta):
"""
Output of this is used to compute joint-to-pose blend shape mapping.
Equation 9 in SMPL paper.
Args:
pose: `Tensor`, N x 72 vector holding the axis-angle rep of K joints.
This includes the global rotation so K=24
Returns
diff_vec : `Tensor`: N x 207 rotation matrix of 23=(K-1) joints with identity subtracted.,
"""
# Ignore global rotation
theta = theta[:,3:]
Rs = batch_rodrigues(torch.reshape(theta, [-1,3]))
lrotmin = torch.reshape(Rs - torch.eye(3), [-1, 207])
return lrotmin
def batch_global_rigid_transformation(Rs, Js, parent, rotate_base=False):
"""
Computes absolute joint locations given pose.
rotate_base: if True, rotates the global rotation by 90 deg in x axis.
if False, this is the original SMPL coordinate.
Args:
Rs: N x 24 x 3 x 3 rotation vector of K joints
Js: N x 24 x 3, joint locations before posing
parent: 24 holding the parent id for each index
Returns
new_J : `Tensor`: N x 24 x 3 location of absolute joints
A : `Tensor`: N x 24 4 x 4 relative joint transformations for LBS.
"""
device = Rs.device
if rotate_base:
print('Flipping the SMPL coordinate frame!!!!')
rot_x = torch.Tensor([[1, 0, 0], [0, -1, 0], [0, 0, -1]])
rot_x = torch.reshape(torch.repeat(rot_x, [N, 1]), [N, 3, 3]) # In tf it was tile
root_rotation = torch.matmul(Rs[:, 0, :, :], rot_x)
else:
root_rotation = Rs[:, 0, :, :]
# Now Js is N x 24 x 3 x 1
Js = Js.unsqueeze(-1)
N = Rs.shape[0]
def make_A(R, t):
# Rs is N x 3 x 3, ts is N x 3 x 1
R_homo = torch.nn.functional.pad(R, (0,0,0,1,0,0))
t_homo = torch.cat([t, torch.ones([N, 1, 1]).to(device=device)], 1)
return torch.cat([R_homo, t_homo], 2)
A0 = make_A(root_rotation, Js[:, 0])
results = [A0]
for i in range(1, parent.shape[0]):
j_here = Js[:, i] - Js[:, parent[i]]
A_here = make_A(Rs[:, i], j_here)
res_here = torch.matmul(
results[parent[i]], A_here)
results.append(res_here)
# 10 x 24 x 4 x 4
results = torch.stack(results, dim=1)
new_J = results[:, :, :3, 3]
# --- Compute relative A: Skinning is based on
# how much the bone moved (not the final location of the bone)
# but (final_bone - init_bone)
# ---
Js_w0 = torch.cat([Js, torch.zeros([N, 35, 1, 1]).to(device=device)], 2)
init_bone = torch.matmul(results, Js_w0)
# Append empty 4 x 3:
init_bone = torch.nn.functional.pad(init_bone, (3,0,0,0,0,0,0,0))
A = results - init_bone
return new_J, A
#########################################################################################
def get_bone_length_scales(part_list, betas_logscale):
leg_joints = list(range(7,11)) + list(range(11,15)) + list(range(17,21)) + list(range(21,25))
tail_joints = list(range(25, 32))
ear_joints = [33, 34]
neck_joints = [15, 6] # ?
core_joints = [4, 5] # ?
mouth_joints = [16, 32]
log_scales = torch.zeros(betas_logscale.shape[0], 35).to(betas_logscale.device)
for ind, part in enumerate(part_list):
if part == 'legs_l':
log_scales[:, leg_joints] = betas_logscale[:, ind][:, None]
elif part == 'tail_l':
log_scales[:, tail_joints] = betas_logscale[:, ind][:, None]
elif part == 'ears_l':
log_scales[:, ear_joints] = betas_logscale[:, ind][:, None]
elif part == 'neck_l':
log_scales[:, neck_joints] = betas_logscale[:, ind][:, None]
elif part == 'core_l':
log_scales[:, core_joints] = betas_logscale[:, ind][:, None]
elif part == 'head_l':
log_scales[:, mouth_joints] = betas_logscale[:, ind][:, None]
else:
pass
all_scales = torch.exp(log_scales)
return all_scales[:, 1:] # don't count root
def get_beta_scale_mask(part_list):
# which joints belong to which bodypart
leg_joints = list(range(7,11)) + list(range(11,15)) + list(range(17,21)) + list(range(21,25))
tail_joints = list(range(25, 32))
ear_joints = [33, 34]
neck_joints = [15, 6] # ?
core_joints = [4, 5] # ?
mouth_joints = [16, 32]
n_b_log = len(part_list) #betas_logscale.shape[1] # 8 # 6
beta_scale_mask = torch.zeros(35, 3, n_b_log) # .to(betas_logscale.device)
for ind, part in enumerate(part_list):
if part == 'legs_l':
beta_scale_mask[leg_joints, [2], [ind]] = 1.0 # Leg lengthening
elif part == 'legs_f':
beta_scale_mask[leg_joints, [0], [ind]] = 1.0 # Leg fatness
beta_scale_mask[leg_joints, [1], [ind]] = 1.0 # Leg fatness
elif part == 'tail_l':
beta_scale_mask[tail_joints, [0], [ind]] = 1.0 # Tail lengthening
elif part == 'tail_f':
beta_scale_mask[tail_joints, [1], [ind]] = 1.0 # Tail fatness
beta_scale_mask[tail_joints, [2], [ind]] = 1.0 # Tail fatness
elif part == 'ears_y':
beta_scale_mask[ear_joints, [1], [ind]] = 1.0 # Ear y
elif part == 'ears_l':
beta_scale_mask[ear_joints, [2], [ind]] = 1.0 # Ear z
elif part == 'neck_l':
beta_scale_mask[neck_joints, [0], [ind]] = 1.0 # Neck lengthening
elif part == 'neck_f':
beta_scale_mask[neck_joints, [1], [ind]] = 1.0 # Neck fatness
beta_scale_mask[neck_joints, [2], [ind]] = 1.0 # Neck fatness
elif part == 'core_l':
beta_scale_mask[core_joints, [0], [ind]] = 1.0 # Core lengthening
# beta_scale_mask[core_joints, [1], [ind]] = 1.0 # Core fatness (height)
elif part == 'core_fs':
beta_scale_mask[core_joints, [2], [ind]] = 1.0 # Core fatness (side)
elif part == 'head_l':
beta_scale_mask[mouth_joints, [0], [ind]] = 1.0 # Head lengthening
elif part == 'head_f':
beta_scale_mask[mouth_joints, [1], [ind]] = 1.0 # Head fatness 0
beta_scale_mask[mouth_joints, [2], [ind]] = 1.0 # Head fatness 1
else:
print(part + ' not available')
raise ValueError
beta_scale_mask = torch.transpose(
beta_scale_mask.reshape(35*3, n_b_log), 0, 1)
return beta_scale_mask
def batch_global_rigid_transformation_biggs(Rs, Js, parent, scale_factors_3x3, rotate_base = False, betas_logscale=None, opts=None):
"""
Computes absolute joint locations given pose.
rotate_base: if True, rotates the global rotation by 90 deg in x axis.
if False, this is the original SMPL coordinate.
Args:
Rs: N x 24 x 3 x 3 rotation vector of K joints
Js: N x 24 x 3, joint locations before posing
parent: 24 holding the parent id for each index
Returns
new_J : `Tensor`: N x 24 x 3 location of absolute joints
A : `Tensor`: N x 24 4 x 4 relative joint transformations for LBS.
"""
if rotate_base:
print('Flipping the SMPL coordinate frame!!!!')
rot_x = torch.Tensor([[1, 0, 0], [0, -1, 0], [0, 0, -1]])
rot_x = torch.reshape(torch.repeat(rot_x, [N, 1]), [N, 3, 3]) # In tf it was tile
root_rotation = torch.matmul(Rs[:, 0, :, :], rot_x)
else:
root_rotation = Rs[:, 0, :, :]
# Now Js is N x 24 x 3 x 1
Js = Js.unsqueeze(-1)
N = Rs.shape[0]
Js_orig = Js.clone()
def make_A(R, t):
# Rs is N x 3 x 3, ts is N x 3 x 1
R_homo = torch.nn.functional.pad(R, (0,0,0,1,0,0))
t_homo = torch.cat([t, torch.ones([N, 1, 1]).to(Rs.device)], 1)
return torch.cat([R_homo, t_homo], 2)
A0 = make_A(root_rotation, Js[:, 0])
results = [A0]
for i in range(1, parent.shape[0]):
j_here = Js[:, i] - Js[:, parent[i]]
try:
s_par_inv = torch.inverse(scale_factors_3x3[:, parent[i]])
except:
# import pdb; pdb.set_trace()
s_par_inv = torch.max(scale_factors_3x3[:, parent[i]], 0.01*torch.eye((3))[None, :, :].to(scale_factors_3x3.device))
rot = Rs[:, i]
s = scale_factors_3x3[:, i]
rot_new = s_par_inv @ rot @ s
A_here = make_A(rot_new, j_here)
res_here = torch.matmul(
results[parent[i]], A_here)
results.append(res_here)
# 10 x 24 x 4 x 4
results = torch.stack(results, dim=1)
# scale updates
new_J = results[:, :, :3, 3]
# --- Compute relative A: Skinning is based on
# how much the bone moved (not the final location of the bone)
# but (final_bone - init_bone)
# ---
Js_w0 = torch.cat([Js_orig, torch.zeros([N, 35, 1, 1]).to(Rs.device)], 2)
init_bone = torch.matmul(results, Js_w0)
# Append empty 4 x 3:
init_bone = torch.nn.functional.pad(init_bone, (3,0,0,0,0,0,0,0))
A = results - init_bone
return new_J, A |