Abstract:
We propose a generative probabilistic model for human motion synthesis. Our model has a hierarchy of three layers. At the bottom layer, we utilize Hidden semi-Markov Model (HSMM), which explicitly models the spatial pose, temporal transition and speed variations in motion sequences. At the middle layer, HSMM parameters are treated as random variables which are allowed to vary across data instances in order to capture large intra- and inter-class variations. At the top layer, hyperparameters define the prior distributions of parameters, preventing the model from overfitting. By explicitly capturing the distribution of the data and parameters, our model has a more compact parameterization compared to GAN-based generative models. We formulate the data synthesis as an adversarial Bayesian inference problem, in which the distributions of generator and discriminator parameters are obtained for data synthesis. We evaluate our method through a variety of metrics, where we show advantage than other competing methods with better fidelity and diversity. We further evaluate the synthesis quality as a data augmentation method for recognition task. Finally, we demonstrate the benefit of our fully probabilistic approach in data restoration task.
