Implicit Neural Representations for Variable Length Human Motion Generation

Understand

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.