Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening

• URL: http://arxiv.org/abs/2409.06791v1
• Date: Tue, 10 Sep 2024 18:02:32 GMT
• Title: Human Motion Synthesis_ A Diffusion Approach for Motion Stitching and In-Betweening
• Authors: Michael Adewole, Oluwaseyi Giwa, Favour Nerrise, Martins Osifeko, Ajibola Oyedeji,
• Abstract summary: 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. We present the performance evaluation of our method using quantitative metrics such as Frechet Inception Distance (FID), Diversity, and Multimodality.
• Score: 2.5165775267615205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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