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

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Submission Number: 8138