Efficient Human Motion Transition via Hybrid Deep Neural Network and Reliable Motion Graph Mining

Skeletal motion transition is of crucial importance to the simulation in interactive environments. In this paper, we propose a hybrid deep learning framework that allows for flexible and efficient human motion transition from motion capture (mocap) data, which optimally satisfies the diverse user-specified paths. We integrate a convolutional restricted Boltzmann machine with deep belief network to detect appropriate transition points. Subsequently, a quadruples-like data structure is exploited for motion graph building, which significantly benefits for the motion splitting and indexing. As a result, various motion clips can be well retrieved and transited fulfilling the user inputs, while preserving the smooth quality of the original data. The experiments show that the proposed transition approach performs favorably compared to the state-of-the-art competing approaches.