Skeleton-in-context: Unified skeleton sequence modeling with in-context learning
In this work, we propose the Skeleton-in-Context, designed to process multiple skeleton-base tasks simultaneously after just one training time. Specifically, we build a novel skeleton-based in-context benchmark covering various tasks. In particular, we propose skeleton prompts composed of TGP and TUP, which solve the overfitting problem of skeleton sequence data trained under the training framework commonly applied in previous 2D and 3D in-context models. Besides, we demonstrate that our model can generalize to different datasets and new tasks, such as motion completion. We hope our research builds the first step in the exploration of in-context learning for skeleton-based sequences, which paves the way for further research in this area.
Dec 15, 2023
