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Prior plays an important role in providing the plausible constraint on humanmotion. Previous works design motion priors following a variety of paradigmsunder different circumstances, leading to the lack of versatility. In thispaper, we first summarize the indispensable properties of the motion prior, andaccordingly, design a framework to learn the versatile motion prior, whichmodels the inherent probability distribution of human motions. Specifically,for efficient prior representation learning, we propose a global orientationnormalization to remove redundant environment information in the originalmotion data space. Also, a two-level, sequence-based and segment-based,frequency guidance is introduced into the encoding stage. Then, we adopt adenoising training scheme to disentangle the environment information from inputmotion data in a learnable way, so as to generate consistent anddistinguishable representation. Embedding our motion prior into prevailingbackbones on three different tasks, we conduct extensive experiments, and bothquantitative and qualitative results demonstrate the versatility andeffectiveness of our motion prior. Our model and code are available athttps://github.com/JchenXu/human-motion-prior.

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance