Context-Aware Inpainter-Refiner for Skeleton-Based Human Motion Completion

In this paper, we propose a context-aware inpainter-refiner (CAIR) framework for skeleton-based human motion completion, which aims to restore the damaged area of a given corrupted sequence in spatio-temporal domain. Conventional methods usually utilize convolutional neural networks to inpaint the corrupted sequence treated as a image. In comparison, we devise a two-stage “inpaiting-refining” framework and further design a context-aware graph convolutional module (CA-GCM), which employs graph convolutional operation and mask-aware residual connection to update values of corrupted area. In this way, our CAIR is capable of mapping and associating intact and corrupted joints implicitly and thus optimize the result generated by our inpainting network effectively. Extensive experimental results on NTU60 and Human3.6M datasets demonstrate that our method outperforms our own baseline and previous methods.