  In this paper, we address the challenging problem of long-term 3D human
motion generation. Specifically, we aim to generate a long sequence of smoothly
connected actions from a stream of multiple sentences (i.e., paragraph).
Previous long-term motion generating approaches were mostly based on recurrent
methods, using previously generated motion chunks as input for the next step.
However, this approach has two drawbacks: 1) it relies on sequential datasets,
which are expensive; 2) these methods yield unrealistic gaps between motions
generated at each step. To address these issues, we introduce simple yet
effective T2LM, a continuous long-term generation framework that can be trained
without sequential data. T2LM comprises two components: a 1D-convolutional
VQVAE, trained to compress motion to sequences of latent vectors, and a
Transformer-based Text Encoder that predicts a latent sequence given an input
text. At inference, a sequence of sentences is translated into a continuous
stream of latent vectors. This is then decoded into a motion by the VQVAE
decoder; the use of 1D convolutions with a local temporal receptive field
avoids temporal inconsistencies between training and generated sequences. This
simple constraint on the VQ-VAE allows it to be trained with short sequences
only and produces smoother transitions. T2LM outperforms prior long-term
generation models while overcoming the constraint of requiring sequential data;
it is also competitive with SOTA single-action generation models.
