  Prior plays an important role in providing the plausible constraint on human
motion. Previous works design motion priors following a variety of paradigms
under different circumstances, leading to the lack of versatility. In this
paper, we first summarize the indispensable properties of the motion prior, and
accordingly, design a framework to learn the versatile motion prior, which
models the inherent probability distribution of human motions. Specifically,
for efficient prior representation learning, we propose a global orientation
normalization to remove redundant environment information in the original
motion data space. Also, a two-level, sequence-based and segment-based,
frequency guidance is introduced into the encoding stage. Then, we adopt a
denoising training scheme to disentangle the environment information from input
motion data in a learnable way, so as to generate consistent and
distinguishable representation. Embedding our motion prior into prevailing
backbones on three different tasks, we conduct extensive experiments, and both
quantitative and qualitative results demonstrate the versatility and
effectiveness of our motion prior. Our model and code are available at
https://github.com/JchenXu/human-motion-prior.
