  Deep generative models are often used for human motion prediction as they are
able to model multi-modal data distributions and characterize diverse human
behavior. While much care has been taken into designing and learning deep
generative models, how to efficiently produce diverse samples from a deep
generative model after it has been trained is still an under-explored problem.
To obtain samples from a pretrained generative model, most existing generative
human motion prediction methods draw a set of independent Gaussian latent codes
and convert them to motion samples. Clearly, this random sampling strategy is
not guaranteed to produce diverse samples for two reasons: (1) The independent
sampling cannot force the samples to be diverse; (2) The sampling is based
solely on likelihood which may only produce samples that correspond to the
major modes of the data distribution. To address these problems, we propose a
novel sampling method, Diversifying Latent Flows (DLow), to produce a diverse
set of samples from a pretrained deep generative model. Unlike random
(independent) sampling, the proposed DLow sampling method samples a single
random variable and then maps it with a set of learnable mapping functions to a
set of correlated latent codes. The correlated latent codes are then decoded
into a set of correlated samples. During training, DLow uses a
diversity-promoting prior over samples as an objective to optimize the latent
mappings to improve sample diversity. The design of the prior is highly
flexible and can be customized to generate diverse motions with common features
(e.g., similar leg motion but diverse upper-body motion). Our experiments
demonstrate that DLow outperforms state-of-the-art baseline methods in terms of
sample diversity and accuracy. Our code is released on the project page:
https://www.ye-yuan.com/dlow.
