In this study, we introduce a learning-based method for generating
high-quality human motion sequences from text descriptions (e.g., ``A person
walks forward"). Existing techniques struggle with motion diversity and smooth
transitions in generating arbitrary-length motion sequences, due to limited
text-to-motion datasets and the pose representations used that often lack
expressiveness or compactness. To address these issues, we propose the first
method for text-conditioned human motion generation in the frequency domain of
motions. We develop a network encoder that converts the motion space into a
compact yet expressive parameterized phase space with high-frequency details
encoded, capturing the local periodicity of motions in time and space with high
accuracy. We also introduce a conditional diffusion model for predicting
periodic motion parameters based on text descriptions and a start pose,
efficiently achieving smooth transitions between motion sequences associated
with different text descriptions. Experiments demonstrate that our approach
outperforms current methods in generating a broader variety of high-quality
motions, and synthesizing long sequences with natural transitions.

本研究介绍一种基于学习的方法，用于从文本描述中生成高质量的人类运动序列，通过在频域上进行文本条件下的人体运动生成，实现了更多样化和平滑的运动序列生成。

DiffusionPhase: 频域中的运动扩散

DiffusionPhase: Motion Diffusion in Frequency Domain

The task of action-driven human motion prediction aims to forecast future
human motion from the observed sequence while respecting the given action
label. It requires modeling not only the stochasticity within human motion but
the smooth yet realistic transition between multiple action labels. However,
the fact that most of the datasets do not contain such transition data
complicates this task. Existing work tackles this issue by learning a
smoothness prior to simply promote smooth transitions, yet doing so can result
in unnatural transitions especially when the history and predicted motions
differ significantly in orientations. In this paper, we argue that valid human
motion transitions should incorporate realistic leg movements to handle
orientation changes, and cast it as an action-conditioned in-betweening (ACB)
learning task to encourage transition naturalness. Because modeling all
possible transitions is virtually unreasonable, our ACB is only performed on
very few selected action classes with active gait motions, such as Walk or Run.
Specifically, we follow a two-stage forecasting strategy by first employing the
motion diffusion model to generate the target motion with a specified future
action, and then producing the in-betweening to smoothly connect the
observation and prediction to eventually address motion prediction. Our method
is completely free from the labeled motion transition data during training. To
show the robustness of our approach, we generalize our trained in-betweening
learning model on one dataset to two unseen large-scale motion datasets to
produce natural transitions. Extensive methods on three benchmark datasets
demonstrate that our method yields the state-of-the-art performance in terms of
visual quality, prediction accuracy, and action faithfulness.

运用动作驱动的过渡模型在人类运动预测中产生平滑的过渡，展示了高效的性能。