  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.
