Motion Compression Using Structurally Connected Neural Network

Motion compression technologies can significantly reduce the redundant information of motion data and increase the efficiency of storage and transmission. Current methods mainly utilize some ready-made universal algorithms, such as signal processing and dimensionality reduction, to model the statistical characteristics of motion data, while the individual structure of motion data is ignored. In this paper, we propose to use a deep neural network with specially designed architecture to represent motion data considering the similarity between the articulated structure of a human skeleton and the architecture of neural networks. The network parameters are then taken as the compressed data. We design a structurally connected network which just looks like a human skeleton. Within the network, only the neurons corresponding to the joints connected to each other in a human skeleton are connected. It effectively exploits the correlations between connected joints to cut down the unnecessary connections between the neurons, which leads to the significant improvement of compression efficiency. Additionally, we extract the two inherent DOFs instead of the original three DOFs of each joint by representing its movement on a sphere according to the rigidity of the articulated human skeleton. This actually achieves the theoretically lossless pre-compression with the ratio of 3:2. Extensive experiment results demonstrate the superior performances of the proposed model at the high compression ratios over other state-of-the-art methods.