Optimizing Motion Completion with Unconstrained Human Skeleton Structure Learning

Completing a motion sequence based on sparse key-frames remains a challenging task. The limited grasp of the human skeleton's spatial structure and the complexity of handling sparsely distributed motion sequences pose challenges for traditional interpolation algorithms, hindering their ability to generate authentic and smooth results. Recent progress utilizes Graph Convolutional Networks (GCN) to analyze human skeleton data, yielding promising results. In this paper, we introduce an improved Attention-Based Graph Convolutional Network framework for motion completion that tackles two key challenges: modeling correlations across indirectly connected joints and modeling correlations across frames in motion sequences with diverse sparsity. The method is designed to augment the learning capability of the GCN-based model without being constrained by the inherent human skeleton structure. Furthermore, this design can be concurrently applied to scenarios involving the completion of both single-person and multi-person motions. Experimental results on public human action datasets NTU RGB-D affirm the Spatio-Temporal Attention-Based Graph Convolutional Network's ability to generate smooth and authentic motion results.