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.
@article{arxiv.2409.06791,
title = {Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening},
author = {Michael Adewole and Oluwaseyi Giwa and Favour Nerrise and Martins Osifeko and Ajibola Oyedeji},
journal= {arXiv preprint arXiv:2409.06791},
year = {2024}
}