  Motion completion is a challenging and long-discussed problem, which is of
great significance in film and game applications. For different motion
completion scenarios (in-betweening, in-filling, and blending), most previous
methods deal with the completion problems with case-by-case designs. In this
work, we propose a simple but effective method to solve multiple motion
completion problems under a unified framework and achieves a new state of the
art accuracy under multiple evaluation settings. Inspired by the recent great
success of attention-based models, we consider the completion as a sequence to
sequence prediction problem. Our method consists of two modules - a standard
transformer encoder with self-attention that learns long-range dependencies of
input motions, and a trainable mixture embedding module that models temporal
information and discriminates key-frames. Our method can run in a
non-autoregressive manner and predict multiple missing frames within a single
forward propagation in real time. We finally show the effectiveness of our
method in music-dance applications.
