  We propose an action-conditional human motion generation method using
variational implicit neural representations (INR). The variational formalism
enables action-conditional distributions of INRs, from which one can easily
sample representations to generate novel human motion sequences. Our method
offers variable-length sequence generation by construction because a part of
INR is optimized for a whole sequence of arbitrary length with temporal
embeddings. In contrast, previous works reported difficulties with modeling
variable-length sequences. We confirm that our method with a Transformer
decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC
datasets in terms of realism and diversity of generated motions. Surprisingly,
even our method with an MLP decoder consistently outperforms the
state-of-the-art Transformer-based auto-encoder. In particular, we show that
variable-length motions generated by our method are better than fixed-length
motions generated by the state-of-the-art method in terms of realism and
diversity. Code at https://github.com/PACerv/ImplicitMotion.
