  Human motion prediction is a classical problem in computer vision and
computer graphics, which has a wide range of practical applications. Previous
effects achieve great empirical performance based on an encoding-decoding
style. The methods of this style work by first encoding previous motions to
latent representations and then decoding the latent representations into
predicted motions. However, in practice, they are still unsatisfactory due to
several issues, including complicated loss constraints, cumbersome training
processes, and scarce switch of different categories of motions in prediction.
In this paper, to address the above issues, we jump out of the foregoing style
and propose a novel framework from a new perspective. Specifically, our
framework works in a masked completion fashion. In the training stage, we learn
a motion diffusion model that generates motions from random noise. In the
inference stage, with a denoising procedure, we make motion prediction
conditioning on observed motions to output more continuous and controllable
predictions. The proposed framework enjoys promising algorithmic properties,
which only needs one loss in optimization and is trained in an end-to-end
manner. Additionally, it accomplishes the switch of different categories of
motions effectively, which is significant in realistic tasks, e.g., the
animation task. Comprehensive experiments on benchmarks confirm the superiority
of the proposed framework. The project page is available at
https://lhchen.top/Human-MAC.
