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Text-to-motion generation has gained increasing attention, but most existingmethods are limited to generating short-term motions that correspond to asingle sentence describing a single action. However, when a text streamdescribes a sequence of continuous motions, the generated motions correspondingto each sentence may not be coherently linked. Existing long-term motiongeneration methods face two main issues. Firstly, they cannot directly generatecoherent motions and require additional operations such as interpolation toprocess the generated actions. Secondly, they generate subsequent actions in anautoregressive manner without considering the influence of future actions onprevious ones. To address these issues, we propose a novel approach thatutilizes a past-conditioned diffusion model with two optional coherent samplingmethods: Past Inpainting Sampling and Compositional Transition Sampling. PastInpainting Sampling completes subsequent motions by treating previous motionsas conditions, while Compositional Transition Sampling models the distributionof the transition as the composition of two adjacent motions guided bydifferent text prompts. Our experimental results demonstrate that our proposedmethod is capable of generating compositional and coherent long-term 3D humanmotions controlled by a user-instructed long text stream. The code is availableat\href{https://github.com/yangzhao1230/PCMDM}{https://github.com/yangzhao1230/PCMDM}.

Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling