Flexible Motion In-betweening with Diffusion Models
<|reference_start|>Flexible Motion In-betweening with Diffusion Models: Motion in-betweening, a fundamental task in character animation, consists of generating motion sequences that plausibly interpolate user-provided keyframe constraints. It has long been recognized as a labor-intensive and challenging process. We investigate the potential of diffusion models in generating diverse human motions guided by keyframes. Unlike previous inbetweening methods, we propose a simple unified model capable of generating precise and diverse motions that conform to a flexible range of user-specified spatial constraints, as well as text conditioning. To this end, we propose Conditional Motion Diffusion In-betweening (CondMDI) which allows for arbitrary dense-or-sparse keyframe placement and partial keyframe constraints while generating high-quality motions that are diverse and coherent with the given keyframes. We evaluate the performance of CondMDI on the text-conditioned HumanML3D dataset and demonstrate the versatility and efficacy of diffusion models for keyframe in-betweening. We further explore the use of guidance and imputation-based approaches for inference-time keyframing and compare CondMDI against these methods.<|reference_end|>
@article{cohan2024flexible,
    title={Flexible Motion In-betweening with Diffusion Models},
    author={Setareh Cohan, Guy Tevet, Daniele Reda, Xue Bin Peng, Michiel van de
  Panne},
    journal={arXiv preprint arXiv:2405.11126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11126},
    primaryClass={cs.CV cs.GR cs.LG}
}