Synthesizing Long-Term Human Motions with Diffusion Models via Coherent
  Sampling
Zhao Yang Bing Su Ji-Rong Wen
Text-to-motion generation has gained increasing attention, but most existing
methods are limited to generating short-term motions that correspond to a
single sentence describing a single action. However, when a text stream
describes a sequence of continuous motions, the generated motions corresponding
to each sentence may not be coherently linked. Existing long-term motion
generation methods face two main issues. Firstly, they cannot directly generate
coherent motions and require additional operations such as interpolation to
process the generated actions. Secondly, they generate subsequent actions in an
autoregressive manner without considering the influence of future actions on
previous ones. To address these issues, we propose a novel approach that
utilizes a past-conditioned diffusion model with two optional coherent sampling
methods: Past Inpainting Sampling and Compositional Transition Sampling. Past
Inpainting Sampling completes subsequent motions by treating previous motions
as conditions, while Compositional Transition Sampling models the distribution
of the transition as the composition of two adjacent motions guided by
different text prompts. Our experimental results demonstrate that our proposed
method is capable of generating compositional and coherent long-term 3D human
motions controlled by a user-instructed long text stream. The code is available
at
\href{https://github.com/yangzhao1230/PCMDM}{https://github.com/yangzhao1230/PCMDM}.