  In the realm of stochastic human motion prediction (SHMP), researchers have
often turned to generative models like GANS, VAEs and diffusion models.
However, most previous approaches have struggled to accurately predict motions
that are both realistic and coherent with past motion due to a lack of guidance
on the latent distribution. In this paper, we introduce Semantic Latent
Directions (SLD) as a solution to this challenge, aiming to constrain the
latent space to learn meaningful motion semantics and enhance the accuracy of
SHMP. SLD defines a series of orthogonal latent directions and represents the
hypothesis of future motion as a linear combination of these directions. By
creating such an information bottleneck, SLD excels in capturing meaningful
motion semantics, thereby improving the precision of motion predictions.
Moreover, SLD offers controllable prediction capabilities by adjusting the
coefficients of the latent directions during the inference phase. Expanding on
SLD, we introduce a set of motion queries to enhance the diversity of
predictions. By aligning these motion queries with the SLD space, SLD is
further promoted to more accurate and coherent motion predictions. Through
extensive experiments conducted on widely used benchmarks, we showcase the
superiority of our method in accurately predicting motions while maintaining a
balance of realism and diversity. Our code and pretrained models are available
at https://github.com/GuoweiXu368/SLD-HMP.
