Text-driven human motion generation is an emerging task in animation and
humanoid robot design. Existing algorithms directly generate the full sequence
which is computationally expensive and prone to errors as it does not pay
special attention to key poses, a process that has been the cornerstone of
animation for decades. We propose KeyMotion, that generates plausible human
motion sequences corresponding to input text by first generating keyframes
followed by in-filling. We use a Variational Autoencoder (VAE) with
Kullback-Leibler regularization to project the keyframes into a latent space to
reduce dimensionality and further accelerate the subsequent diffusion process.
For the reverse diffusion, we propose a novel Parallel Skip Transformer that
performs cross-modal attention between the keyframe latents and text condition.
To complete the motion sequence, we propose a text-guided Transformer designed
to perform motion-in-filling, ensuring the preservation of both fidelity and
adherence to the physical constraints of human motion. Experiments show that
our method achieves state-of-theart results on the HumanML3D dataset
outperforming others on all R-precision metrics and MultiModal Distance.
KeyMotion also achieves competitive performance on the KIT dataset, achieving
the best results on Top3 R-precision, FID, and Diversity metrics.

提出了一种名为 KeyMotion 的方法，通过生成关键帧并进行填充，实现根据输入文本生成逼真的人体运动序列。通过使用具有 Kullback-Leibler 正则化的变分自编码器（VAE）将关键帧投影到潜空间，来减少维度和加速扩散过程。同时，引入了一种新的并行跳过注意力机制的 Transformer，用于实现关键帧潜向量和文本条件之间的跨模态注意力。通过引入文本引导的 Transformer 进行动作填充，确保运动序列的保真度和遵循人体运动的物理约束。实验证明，该方法在 HumanML3D 数据集上达到了最先进的结果，所有 R-Precision 度量和多模态距离指标均优于其他方法。同时，在 KIT 数据集上也获得了有竞争力的性能，在 Top3 R-Precision、FID 和多样性度量指标上取得了最佳结果。