r"""Applies a spatial temporal graph convolution over an input graph sequence. Args: in_channels (int): Number of channels in the input se

r"""Applies a spatial temporal graph convolution over an input graph sequence. Args: in_channels (int): Number of channels in the input se

r"""The basic module for applying a graph convolution. Args: in_channels (int): Number of channels in the input sequence data out_

Implementation of SMPLify, use 3D joints.

r"""Spatial temporal graph convolutional networks. Args: in_channels (int): Number of channels in the input data num_class (int):

r"""Spatial temporal graph convolutional networks. Args: in_channels (int): Number of channels in the input data num_class (int):

Contrastive loss function. Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

Utilities for training and sampling diffusion models. Ported directly from here, and then adapted over time to further experimentation.

The Graph to model the skeletons extracted by the openpose Args: strategy (string): must be one of the follow candidates - unifor

The Graph to model the skeletons extracted by the openpose Args: strategy (string): must be one of the follow candidates - unifor

Extension of the official SMPL implementation to support more joints

A diffusion process which can skip steps in a base diffusion process. :param use_timesteps: a collection (sequence or set) of timesteps from

Dumps key/value pairs into TensorBoard's numeric format.

Which type of output the model predicts.

What is used as the model's output variance. The LEARNED_RANGE option has been added to allow the model to predict values between FIXED_

A distribution over timesteps in the diffusion process, intended to reduce variance of the objective. By default, samplers perform unbia