Implicit Neural Representations for Variable Length Human Motion Generation

• URL: http://arxiv.org/abs/2203.13694v1
• Date: Fri, 25 Mar 2022 15:00:38 GMT
• Title: Implicit Neural Representations for Variable Length Human Motion Generation
• Authors: Pablo Cervantes and Yusuke Sekikawa and Ikuro Sato and Koichi Shinoda
• Abstract summary: We propose an action-conditional human motion generation method using variational implicit neural representations (INR) 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. 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.
• Score: 11.028791809955276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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