Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening
<|reference_start|>Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening: Human motion generation is an important area of research in many fields. In this work, we tackle the problem of motion stitching and in-betweening. Current methods either require manual efforts, or are incapable of handling longer sequences. To address these challenges, we propose a diffusion model with a transformer-based denoiser to generate realistic human motion. Our method demonstrated strong performance in generating in-betweening sequences, transforming a variable number of input poses into smooth and realistic motion sequences consisting of 75 frames at 15 fps, resulting in a total duration of 5 seconds. We present the performance evaluation of our method using quantitative metrics such as Frechet Inception Distance (FID), Diversity, and Multimodality, along with visual assessments of the generated outputs.<|reference_end|>
@article{adewole2024human,
    title={Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and
  In-Betweening},
    author={Michael Adewole, Oluwaseyi Giwa, Favour Nerrise, Martins Osifeko, and
  Ajibola Oyedeji},
    journal={arXiv preprint arXiv:2409.06791},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06791},
    primaryClass={cs.CV cs.HC cs.LG}
}