File size: 7,127 Bytes
e34aada |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
import torch
import torch.nn.functional as F
def rotation_matrix_x(theta):
theta = theta.reshape(-1, 1, 1)
z = torch.zeros_like(theta)
o = torch.ones_like(theta)
c = torch.cos(theta)
s = torch.sin(theta)
return torch.cat(
[
torch.cat([c, z, s], 2),
torch.cat([z, o, z], 2),
torch.cat([-s, z, c], 2),
],
1,
)
def rotation_matrix_y(theta):
theta = theta.reshape(-1, 1, 1)
z = torch.zeros_like(theta)
o = torch.ones_like(theta)
c = torch.cos(theta)
s = torch.sin(theta)
return torch.cat(
[
torch.cat([o, z, z], 2),
torch.cat([z, c, -s], 2),
torch.cat([z, s, c], 2),
],
1,
)
def rotation_matrix_z(theta):
theta = theta.reshape(-1, 1, 1)
z = torch.zeros_like(theta)
o = torch.ones_like(theta)
c = torch.cos(theta)
s = torch.sin(theta)
return torch.cat(
[
torch.cat([c, -s, z], 2),
torch.cat([s, c, z], 2),
torch.cat([z, z, o], 2),
],
1,
)
def transform_kp(canonical_kp, yaw, pitch, roll, t, delta):
# [N,K,3] [N,] [N,] [N,] [N,3] [N,K,3]
# y, x, z
# w, h, d
rot_mat = rotation_matrix_y(pitch) @ rotation_matrix_x(yaw) @ rotation_matrix_z(roll)
transformed_kp = torch.matmul(rot_mat.unsqueeze(1), canonical_kp.unsqueeze(-1)).squeeze(-1) + t.unsqueeze(1) + delta
return transformed_kp, rot_mat
def transform_kp_with_new_pose(canonical_kp, yaw, pitch, roll, t, delta, new_yaw, new_pitch, new_roll):
# [N,K,3] [N,] [N,] [N,] [N,3] [N,K,3]
# y, x, z
# w, h, d
old_rot_mat = rotation_matrix_y(pitch) @ rotation_matrix_x(yaw) @ rotation_matrix_z(roll)
rot_mat = rotation_matrix_y(new_pitch) @ rotation_matrix_x(new_yaw) @ rotation_matrix_z(new_roll)
R = torch.matmul(rot_mat, torch.inverse(old_rot_mat))
transformed_kp = (
torch.matmul(rot_mat.unsqueeze(1), canonical_kp.unsqueeze(-1)).squeeze(-1)
+ t.unsqueeze(1)
+ torch.matmul(R.unsqueeze(1), delta.unsqueeze(-1)).squeeze(-1)
)
zt = 0.33 - transformed_kp[:, :, 2].mean()
transformed_kp = transformed_kp + torch.FloatTensor([0, 0, zt]).cuda()
return transformed_kp, rot_mat
def make_coordinate_grid_2d(spatial_size):
h, w = spatial_size
x = torch.arange(h).cuda()
y = torch.arange(w).cuda()
x = 2 * (x / (h - 1)) - 1
y = 2 * (y / (w - 1)) - 1
xx = x.reshape(-1, 1).repeat(1, w)
yy = y.reshape(1, -1).repeat(h, 1)
meshed = torch.cat([yy.unsqueeze(2), xx.unsqueeze(2)], 2)
return meshed
def make_coordinate_grid_3d(spatial_size):
d, h, w = spatial_size
z = torch.arange(d).cuda()
x = torch.arange(h).cuda()
y = torch.arange(w).cuda()
z = 2 * (z / (d - 1)) - 1
x = 2 * (x / (h - 1)) - 1
y = 2 * (y / (w - 1)) - 1
zz = z.reshape(-1, 1, 1).repeat(1, h, w)
xx = x.reshape(1, -1, 1).repeat(d, 1, w)
yy = y.reshape(1, 1, -1).repeat(d, h, 1)
meshed = torch.cat([yy.unsqueeze(3), xx.unsqueeze(3), zz.unsqueeze(3)], 3)
return meshed
def out2heatmap(out, temperature=0.1):
final_shape = out.shape
heatmap = out.reshape(final_shape[0], final_shape[1], -1)
heatmap = F.softmax(heatmap / temperature, dim=2)
heatmap = heatmap.reshape(*final_shape)
return heatmap
def heatmap2kp(heatmap):
shape = heatmap.shape
grid = make_coordinate_grid_3d(shape[2:]).unsqueeze(0).unsqueeze(0)
kp = (heatmap.unsqueeze(-1) * grid).sum(dim=(2, 3, 4))
return kp
def kp2gaussian_2d(kp, spatial_size, kp_variance=0.01):
N, K = kp.shape[:2]
coordinate_grid = make_coordinate_grid_2d(spatial_size).reshape(1, 1, *spatial_size, 2).repeat(N, K, 1, 1, 1)
mean = kp.reshape(N, K, 1, 1, 2)
mean_sub = coordinate_grid - mean
out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
return out
def kp2gaussian_3d(kp, spatial_size, kp_variance=0.01):
N, K = kp.shape[:2]
coordinate_grid = make_coordinate_grid_3d(spatial_size).reshape(1, 1, *spatial_size, 3).repeat(N, K, 1, 1, 1, 1)
mean = kp.reshape(N, K, 1, 1, 1, 3)
mean_sub = coordinate_grid - mean
out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
return out
def create_heatmap_representations(fs, kp_s, kp_d):
spatial_size = fs.shape[2:]
heatmap_d = kp2gaussian_3d(kp_d, spatial_size)
heatmap_s = kp2gaussian_3d(kp_s, spatial_size)
heatmap = heatmap_d - heatmap_s
zeros = torch.zeros(heatmap.shape[0], 1, *spatial_size).cuda()
# [N,21,16,64,64]
heatmap = torch.cat([zeros, heatmap], dim=1)
# [N,21,1,16,64,64]
heatmap = heatmap.unsqueeze(2)
return heatmap
def create_sparse_motions(fs, kp_s, kp_d, Rs, Rd):
N, _, D, H, W = fs.shape
K = kp_s.shape[1]
identity_grid = make_coordinate_grid_3d((D, H, W)).reshape(1, 1, D, H, W, 3).repeat(N, 1, 1, 1, 1, 1)
# [N,20,16,64,64,3]
coordinate_grid = identity_grid.repeat(1, K, 1, 1, 1, 1) - kp_d.reshape(N, K, 1, 1, 1, 3)
# [N,1,1,1,1,3,3]
jacobian = torch.matmul(Rs, torch.inverse(Rd)).unsqueeze(-3).unsqueeze(-3).unsqueeze(-3).unsqueeze(-3)
coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)).squeeze(-1)
driving_to_source = coordinate_grid + kp_s.reshape(N, K, 1, 1, 1, 3)
sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)
# sparse_motions = driving_to_source
# [N,21,16,64,64,3]
return sparse_motions
def create_deformed_source_image2d(fs, sparse_motions):
N, _, H, W = fs.shape
K = sparse_motions.shape[1] - 1
# [N*21,4,16,64,64]
source_repeat = fs.unsqueeze(1).repeat(1, K + 1, 1, 1, 1).reshape(N * (K + 1), -1, H, W)
# [N*21,16,64,64,3]
sparse_motions = sparse_motions.reshape((N * (K + 1), H, W, -1))
# [N*21,4,16,64,64]
sparse_deformed = F.grid_sample(source_repeat, sparse_motions, align_corners=True)
sparse_deformed = sparse_deformed.reshape((N, K + 1, -1, H, W))
# [N,21,4,16,64,64]
return sparse_deformed
def create_deformed_source_image(fs, sparse_motions):
N, _, D, H, W = fs.shape
K = sparse_motions.shape[1] - 1
# [N*21,4,16,64,64]
source_repeat = fs.unsqueeze(1).repeat(1, K + 1, 1, 1, 1, 1).reshape(N * (K + 1), -1, D, H, W)
# [N*21,16,64,64,3]
sparse_motions = sparse_motions.reshape((N * (K + 1), D, H, W, -1))
# [N*21,4,16,64,64]
sparse_deformed = F.grid_sample(source_repeat, sparse_motions, align_corners=True)
sparse_deformed = sparse_deformed.reshape((N, K + 1, -1, D, H, W))
# [N,21,4,16,64,64]
return sparse_deformed
def apply_imagenet_normalization(input):
mean = input.new_tensor([0.485, 0.456, 0.406]).reshape(1, 3, 1, 1)
std = input.new_tensor([0.229, 0.224, 0.225]).reshape(1, 3, 1, 1)
output = (input - mean) / std
return output
def apply_vggface_normalization(input):
mean = input.new_tensor([129.186279296875, 104.76238250732422, 93.59396362304688]).reshape(1, 3, 1, 1)
std = input.new_tensor([1, 1, 1]).reshape(1, 3, 1, 1)
output = (input * 255 - mean) / std
return output
|