Length-Aware Motion Synthesis via Latent Diffusion
<|reference_start|>Length-Aware Motion Synthesis via Latent Diffusion: The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is not merely fast-forwarding it. However, state-of-the-art techniques for human behavior synthesis have limited control over the target sequence length.   We introduce the problem of generating length-aware 3D human motion sequences from textual descriptors, and we propose a novel model to synthesize motions of variable target lengths, which we dub "Length-Aware Latent Diffusion" (LADiff). LADiff consists of two new modules: 1) a length-aware variational auto-encoder to learn motion representations with length-dependent latent codes; 2) a length-conforming latent diffusion model to generate motions with a richness of details that increases with the required target sequence length. LADiff significantly improves over the state-of-the-art across most of the existing motion synthesis metrics on the two established benchmarks of HumanML3D and KIT-ML.<|reference_end|>
@article{sampieri2024length-aware,
    title={Length-Aware Motion Synthesis via Latent Diffusion},
    author={Alessio Sampieri, Alessio Palma, Indro Spinelli, Fabio Galasso},
    journal={arXiv preprint arXiv:2407.11532},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11532},
    primaryClass={cs.CV}
}