The emergence of text-driven motion synthesis technique provides animators
with great potential to create efficiently. However, in most cases, textual
expressions only contain general and qualitative motion descriptions, while
lack fine depiction and sufficient intensity, leading to the synthesized
motions that either (a) semantically compliant but uncontrollable over specific
pose details, or (b) even deviates from the provided descriptions, bringing
animators with undesired cases. In this paper, we propose DiffKFC, a
conditional diffusion model for text-driven motion synthesis with keyframes
collaborated. Different from plain text-driven designs, full interaction among
texts, keyframes and the rest diffused frames are conducted at training,
enabling realistic generation under efficient, collaborative dual-level
control: coarse guidance at semantic level, with only few keyframes for direct
and fine-grained depiction down to body posture level, to satisfy animator
requirements without tedious labor. Specifically, we customize efficient
Dilated Mask Attention modules, where only partial valid tokens participate in
local-to-global attention, indicated by the dilated keyframe mask. For user
flexibility, DiffKFC supports adjustment on importance of fine-grained keyframe
control. Experimental results show that our model achieves state-of-the-art
performance on text-to-motion datasets HumanML3D and KIT.

本文提出了一种基于文本的运动合成技术，使用 DiffKFC 建立了一个条件扩散模型，该模型结合了关键帧来协作控制运动，通过 Dilated Mask Attention 模块来实现对身体姿势的微观描述，以实现高效、协同、粗细粒度的生成运动的目的。实验表明，本模型在人机交互 3D 和 KIT 等文本到运动数据集上表现出了最先进的性能。