Deep Human Dynamics Prior

Motion capture (MoCap) technology aims to provide an accurate record of human motion, with specific potentials in activity analysis, human behavior understanding, as well as multimedia industries of animation production and special effects movies. However, because of joint occlusion and limitation of equipment precision, the raw motion data are often damaged, which severely hinders its downstream applications. The latest method relies on deep neural networks to reconstruct the underlying complete motion from the degraded observation, achieving remarkable results. Unfortunately, due to the non-enumerability of human motion, the trained model from large-scale training data often fails to comprehensively cover incomputable action categories, which may lead to a sharp decline in the performance of deep learning-based methods. To handle these limitations, we propose an untrained deep generative model, in which Graph Convolutional Networks (GCNs) are utilized to efficiently capture complicated topological relationships of human joints. We show that the untrained GCN architecture with randomly-initialized weights is sufficient to extract some low-level statistics for human motion reconstruction without any training process. Notably, the performance of our approach is comparable to that of those trained models, while its application is not restricted by the availability of training data or a pre-trained network. Moreover, the proposed model even surpasses the state-of-the-art methods when encountering unprecedented samples in the human action database, regardless of the tasks of human motion recovery and gap-filling problem.