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 this https URL

本研究提出了一种基于变分隐式神经表示的行动条件人体动作生成方法，可通过变分形式生成变长人体动作序列，使用 Transformer 解码器的方法在生成真实且多样的动作序列方面胜过了先前的方法，甚至优于最先进的基于 Transformer 自编码器方法。