PhaseFusion: A Diffusion-based Periodic Parameterized Motion Generation Framework

Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu

Keywords: Motion Synthesis, Text to Motion

TL;DR: A Diffusion-based Periodic Parameterized Motion Generation Framework

Abstract: In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions due to limited text-to-motion datasets and reliance on full-body skeletal pose representations. To address this, we develop a network encoder that converts motion sequences into periodic signals, capturing the local periodicity of motions in time and space. We also propose a conditional diffusion model for predicting periodic motion parameters based on text descriptions and the starting pose. Our approach outperforms current methods, generating a broader variety of high-quality motions with natural transitions, especially in longer sequences.

Primary Area: generative models

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 8138