  In this paper we propose a convolutional autoencoder to address the problem
of motion infilling for 3D human motion data. Given a start and end sequence,
motion infilling aims to complete the missing gap in between, such that the
filled in poses plausibly forecast the start sequence and naturally transition
into the end sequence. To this end, we propose a single, end-to-end trainable
convolutional autoencoder. We show that a single model can be used to create
natural transitions between different types of activities. Furthermore, our
method is not only able to fill in entire missing frames, but it can also be
used to complete gaps where partial poses are available (e.g. from end
effectors), or to clean up other forms of noise (e.g. Gaussian). Also, the
model can fill in an arbitrary number of gaps that potentially vary in length.
In addition, no further post-processing on the model's outputs is necessary
such as smoothing or closing discontinuities at the end of the gap. At the
heart of our approach lies the idea to cast motion infilling as an inpainting
problem and to train a convolutional de-noising autoencoder on image-like
representations of motion sequences. At training time, blocks of columns are
removed from such images and we ask the model to fill in the gaps. We
demonstrate the versatility of the approach via a number of complex motion
sequences and report on thorough evaluations performed to better understand the
capabilities and limitations of the proposed approach.
