  We propose to leverage Transformer architectures for non-autoregressive human
motion prediction. Our approach decodes elements in parallel from a query
sequence, instead of conditioning on previous predictions such as
instate-of-the-art RNN-based approaches. In such a way our approach is less
computational intensive and potentially avoids error accumulation to long term
elements in the sequence. In that context, our contributions are fourfold: (i)
we frame human motion prediction as a sequence-to-sequence problem and propose
a non-autoregressive Transformer to infer the sequences of poses in parallel;
(ii) we propose to decode sequences of 3D poses from a query sequence generated
in advance with elements from the input sequence;(iii) we propose to perform
skeleton-based activity classification from the encoder memory, in the hope
that identifying the activity can improve predictions;(iv) we show that despite
its simplicity, our approach achieves competitive results in two public
datasets, although surprisingly more for short term predictions rather than for
long term ones.
