Authors
- Kengo Uchida
- Takashi Shibuya
- Yuhta Takida
- Naoki Murata
- Julian Tanke
- Shusuke Takahashi*
- Yuki Mitsufuji
* External authors
Venue
- CVPR-25
Date
- 2025
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MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training
Julian Tanke
Shusuke Takahashi*
* External authors
CVPR-25
2025
Abstract
In text-to-motion generation, controllability as well as generation quality and speed has become increasingly critical. The controllability challenges include generating a motion of a length that matches the given textual description and editing the generated motions according to control signals, such as the start-end positions and the pelvis trajectory. In this paper, we propose MoLA, which provides fast, high-quality, variable-length motion generation and can also deal with multiple editing tasks in a single framework. Our approach revisits the motion representation used as inputs and outputs in the model, incorporating an activation variable to enable variable-length motion generation. Additionally, we integrate a variational autoencoder and a latent diffusion model, further enhanced through adversarial training, to achieve high-quality and fast generation. Moreover, we apply a training-free guided generation framework to achieve various editing tasks with motion control inputs. We quantitatively show the effectiveness of adversarial learning in text-to-motion generation, and demonstrate the applicability of our editing framework to multiple editing tasks in the motion domain.
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