@article{humanmotiondiffusionmodel2023,
  title = {Human Motion Diffusion Model (MDM)},
  author = {Tevet, G. and Raab, S. and Gordon, B. and Shafir, Y. and Cohen-Or, D. and Bermano, A. H.},
  year = {2023},
  journal = {ICLR 2023 (Oral)},
  url = {https://arxiv.org/abs/2209.14916},
  abstract = {Tevet et al. (2022, published ICLR 2023 Oral) introduce the Motion Diffusion Model, a transformer-based diffusion model that operates directly on motion sequences and accepts conditioning from text prompts, action labels, or partial keyframes. The approach produces diverse, naturalistic human motions without requiring a motion prior or complex post-processing. As the founding paper in text-to-motion diffusion, MDM established the architecture underlying a generation of motion-generation tools that are entering character animation and virtual-production pipelines.},
  keywords = {character-animation, motion-synthesis, generative-ai},
  note = {AI \& Animation Education Knowledge Base}
}