Abstract

Motion interpolation technology produces transition motion frames between two discrete movements. It is wildly used in video games, virtual reality and augmented reality. In the fields of computer graphics and animations, our data-driven method generates transition motions of two arbitrary animations without additional control signals. In this work, we propose a novel carefully designed deep learning framework, named deep motion interpolation network (DMIN), to learn human movement habits from a real dataset and then to per- form the interpolation function specific for human motions. It is a data-driven approach to capture overall rhythm of two given discrete movements and gener- ate natural in-between motion frames. The sequence-by-sequence architecture allows completing all missing frames within single forward inference, which reduces computation time for interpolation. Experiments on human motion datasets show that our network achieves promising interpolation performance. The ablation study demonstrates the effectiveness of the carefully designed DMIN.