Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor
This work addresses motion prediction for practical applications by enabling few-shot learning without domain-specific training, though it is incremental in leveraging existing language models.
The paper tackles few-shot human motion prediction by proposing a training-free framework that uses off-the-shelf ChatGPT, achieving competitive performance without dedicated model training.
To facilitate the application of motion prediction in practice, recently, the few-shot motion prediction task has attracted increasing research attention. Yet, in existing few-shot motion prediction works, a specific model that is dedicatedly trained over human motions is generally required. In this work, rather than tackling this task through training a specific human motion prediction model, we instead propose a novel FMP-OC framework. In FMP-OC, in a totally training-free manner, we enable Few-shot Motion Prediction, which is a non-language task, to be performed directly via utilizing the Off-the-shelf language model ChatGPT. Specifically, to lead ChatGPT as a language model to become an accurate motion predictor, in FMP-OC, we first introduce several novel designs to facilitate extracting implicit knowledge from ChatGPT. Moreover, we also incorporate our framework with a motion-in-context learning mechanism. Extensive experiments demonstrate the efficacy of our proposed framework.