| Autor: |
Wan, Weilin, Huang, Yiming, Wu, Shutong, Komura, Taku, Wang, Wenping, Jayaraman, Dinesh, Liu, Lingjie |
| Rok vydání: |
2023 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
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 in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
