Abstract
Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In this paper, we propose a diffusion-based motion synthesis and editing model named FLAME. Inspired by the recent successes in diffusion models, we integrate diffusion-based generative models into the motion domain. FLAME can generate highfdelity motions well aligned with the given text. Also, it can edit the parts of the motion, both frame-wise and joint-wise, without any fne-tuning. FLAME involves a new transformerbased architecture we devise to better handle motion data, which is found to be crucial to manage variable-length motions and well attend to free-form text. In experiments, we show that FLAME achieves state-of-the-art generation performances on three text-motion datasets: HumanML3D, BABEL, and KIT. We also demonstrate that FLAME’s editing capability can be extended to other tasks such as motion prediction or motion in-betweening, which have been previously covered by dedicated models.
Original language | English |
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Title of host publication | AAAI-23 Technical Tracks 7 |
Editors | Brian Williams, Yiling Chen, Jennifer Neville |
Publisher | AAAI press |
Pages | 8255-8263 |
Number of pages | 9 |
ISBN (Electronic) | 9781577358800 |
DOIs | |
Publication status | Published - 2023 Jun 27 |
Event | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 2023 Feb 7 → 2023 Feb 14 |
Publication series
Name | Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Volume | 37 |
Conference
Conference | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
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Country/Territory | United States |
City | Washington |
Period | 23/2/7 → 23/2/14 |
Bibliographical note
Publisher Copyright:Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence