  In this paper, we present a deep generative model based method to generate
diverse human motion interpolation results. We resort to the Conditional
Variational Auto-Encoder (CVAE) to learn human motion conditioned on a pair of
given start and end motions, by leveraging the Recurrent Neural Network (RNN)
structure for both the encoder and the decoder. Additionally, we introduce a
regularization loss to further promote sample diversity. Once trained, our
method is able to generate multiple plausible coherent motions by repetitively
sampling from the learned latent space. Experiments on the publicly available
dataset demonstrate the effectiveness of our method, in terms of sample
plausibility and diversity.
