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Large Language Models(LLMs) have shown remarkable emergent abilities inunifying almost all (if not every) NLP tasks. In the human motion-relatedrealm, however, researchers still develop siloed models for each task. Inspiredby InstuctGPT, and the generalist concept behind Gato, we introduce AvatarGPT,an All-in-One framework for motion understanding, planning, generations as wellas other tasks such as motion in-between synthesis. AvatarGPT treats each taskas one type of instruction fine-tuned on the shared LLM. All the tasks areseamlessly interconnected with language as the universal interface,constituting a closed-loop within the framework. To achieve this, human motionsequences are first encoded as discrete tokens, which serve as the extendedvocabulary of LLM. Then, an unsupervised pipeline to generate natural languagedescriptions of human action sequences from in-the-wild videos is developed.Finally, all tasks are jointly trained. Extensive experiments show thatAvatarGPT achieves SOTA on low-level tasks, and promising results on high-leveltasks, demonstrating the effectiveness of our proposed All-in-One framework.Moreover, for the first time, AvatarGPT enables a principled approach byiterative traversal of the tasks within the closed-loop for unlimitedlong-motion synthesis.

AvatarGPT: All-in-One Framework for Motion Understanding, Planning, Generation and Beyond