Recent work has demonstrated the significant potential of denoising diffusion models for generating human motion, including text-to-motion capabilities. However, these methods are restricted by the paucity of annotated motion data, a focus on single-person motions, and a lack of detailed control. In this paper, we introduce three forms of composition based on diffusion priors: sequential, parallel, and model composition. Using sequential composition, we tackle the challenge of long sequence generation. We introduce DoubleTake, an inference-time method with which we generate long animations consisting of sequences of prompted intervals and their transitions, using a prior trained only for short clips. Using parallel composition, we show promising steps toward two-person generation. Beginning with two fixed priors as well as a few two-person training examples, we learn a slim communication block, ComMDM, to coordinate interaction between the two resulting motions. Lastly, using model composition, we first train individual priors to complete motions that realize a prescribed motion for a given joint. We then introduce DiffusionBlending, an interpolation mechanism to effectively blend several such models to enable flexible and efficient fine-grained joint and trajectory-level control and editing. We evaluate the composition methods using an off-the-shelf motion diffusion model, and further compare the results to dedicated models trained for these specific tasks.

翻译：近期研究已证明去噪扩散模型在生成人体运动（包括文本到运动能力）方面具有显著潜力。然而，这些方法受限于标注运动数据的匮乏、仅关注单人运动以及缺乏精细控制。本文基于扩散先验引入三种组合形式：顺序组合、并行组合和模型组合。利用顺序组合，我们解决了长序列生成难题。提出DoubleTake方法——一种推理阶段技术，通过仅针对短片段训练的先验，生成包含提示区间及其过渡的长动画序列。借助并行组合，我们在双人运动生成方面取得了初步进展。基于两个固定先验和少量双人训练样本，我们学习了一套轻量级通信模块ComMDM，用于协调两段生成运动间的交互。最后通过模型组合，我们首先训练独立先验以完成指定关节的预定运动，随后提出DiffusionBlending插值机制，有效融合多个此类模型，实现灵活高效的细粒度关节与轨迹级控制及编辑。我们采用现成运动扩散模型评估组合方法，并将结果与针对特定任务训练的专用模型进行对比。