Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).

Return multiplication of two quaternions. >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7]) >>> numpy.allclose(q, [28, -44, -14, 48])

This function is borrowed from https://github.com/kornia/kornia Convert 3x4 rotation matrix to Rodrigues vector Args: rotation_

Take the mean over all non-batch dimensions.

Compute the mean and variance of the diffusion posterior: q(x_{t-1} | x_t, x_0)

Construct 3D identity rotation given shape

pytorch acos nan: https://github.com/pytorch/pytorch/issues/8069

Return a tensor where each element has the absolute value taken from the, corresponding element of a, with sign taken from the corresponding

Returns torch.sqrt(torch.max(0, x)) but with a zero subgradient where x is 0.

Read the object from a file (either .npy or .json) :param path: path of the file :type path: string :param args, kwargs: the

Construct a skeleton motion from a skeleton state. The velocities are estimated using second order guassian filter along the last axi

Diffuse the dataset for a given number of diffusion steps. In other words, sample from q(x_t | x_0). :param x_start: the in

Enforce quaternion continuity across the time dimension by selecting the representation (q or -q) with minimal distance (or, equivalently, ma

Return quaternion for rotation about axis. >>> q = quaternion_about_axis(0.123, [1, 0, 0]) >>> numpy.allclose(q, [0.99810947, 0.06146124, 0,

Return inverse of quaternion. >>> q0 = random_quaternion() >>> q1 = quaternion_inverse(q0) >>> numpy.allclose(quaternion_multiply(q0, q1)

q: size(Bx4) Output: size(Bx4)

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

Update the plotter for any updates in the task primitives