1. Length-Aware Motion Synthesis via Latent Diffusion

    European Conference on Computer Vision (ECCV) 2024

    Alessio Sampieri, Alessio Palma, Indro Spinelli, Fabio Galasso

    Paper
Length-Aware Motion Synthesis via Latent Diffusion

  1. Abstract

    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.

  1. Citation

    If you find this work useful in your research, please cite our paper:

    @inproceedings{sampieri2024lengthawaremotionsynthesislatent,
  title = {Length-Aware Motion Synthesis via Latent Diffusion},
  author = {Sampieri, Alessio and Palma, Alessio and Spinelli, Indro and Galasso, Fabio},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2024},
  organization = {Springer},
  eprint = {2407.11532},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  url = {https://arxiv.org/abs/2407.11532},
  bibtex_show = true
}