Applies a batch of rigid transformations to the joints Parameters ---------- rot_mats : torch.tensor BxNx3x3 Tensor of rotat

same as batch_matrix2axis Calculates the rotation matrices for a batch of rotation vectors Parameters ---------- rot_vec

Calculates the per vertex displacement due to the blend shapes Parameters ---------- betas : torch.tensor Bx(num_betas) Blend s

Calculates the per vertex displacement due to the blend shapes Parameters ---------- betas : torch.tensor Bx(num_betas) Blend s

Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding

Runs MICA on the input image and calculates the L2 loss between the input shape_params and the shape_params calculated by MICA.

1x1 convolution

for batch image Args: image (torch.Tensor): the reference tensor of shape BXHxWXC. center: [bz, 2] bboxsize: [bz, 1]

Convert a flattened parameter vector to a dictionary of parameters code_dict.keys() = ['shape', 'tex', 'exp', 'pose', 'cam', 'light']

Get a yacs CfgNode object with default values for my_project.

r"""Function that returns Gaussian filter matrix coefficients. Args: kernel_size (Tuple[int, int]): filter sizes in the x and y direction

r"""Function that returns Gaussian filter matrix coefficients. Args: kernel_size (int): filter size should be odd. Returns:

Keep specified vertices in the mesh and update the faces. Parameters: faces (torch.Tensor): Tensor of shape (F, 3) representing faces, w

landmarks_interpolate. :param landmarks: List, the raw landmark (in-place)

Performs Linear Blend Skinning with the given shape and pose parameters Parameters ---------- betas : torch.tensor BxNB

Performs Linear Blend Skinning with the given shape and pose parameters Parameters ---------- betas : torch.tensor BxNB

linear_interpolate. :param landmarks: ndarray, input landmarks to be interpolated. :param start_idx: int, the start index for linear interpol

linear_interpolate. :param landmarks: ndarray, input landmarks to be interpolated. :param start_idx: int, the start index for linear interpol

Create a grid of images from the list of cv2 images in images

Parse input arguments.

Parse input arguments.

Parse input arguments.

Parse input arguments.

Draw 68 key points Args: image: the input image kpt: (68, 3).

Constructs a ResNet-50 model.

Convert 3x4 rotation matrix to 4d quaternion vector This algorithm is based on algorithm described in https://github.com/KieranWynn/pyquatern

:param videos: list of videos :param transcriptions: list of transcriptions :return:

save2avi. - function taken from Visual Speech Recognition repository :param filename: str, the filename to save the video (.avi). :param data