MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training
<|reference_start|>MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training: In motion generation, controllability as well as generation quality and speed is becoming more and more important. There are various motion editing tasks, such as in-betweening, upper body editing, and path-following, but existing methods perform motion editing with a data-space diffusion model, which is slow in inference compared to a latent diffusion model. In this paper, we propose MoLA, which provides fast and high-quality motion generation and also can deal with multiple editing tasks in a single framework. For high-quality and fast generation, we employ a variational autoencoder and latent diffusion model, and improve the performance with adversarial training. In addition, 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.<|reference_end|>
@article{uchida2024mola:,
    title={MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by
  Adversarial Training},
    author={Kengo Uchida, Takashi Shibuya, Yuhta Takida, Naoki Murata, Shusuke
  Takahashi, Yuki Mitsufuji},
    journal={arXiv preprint arXiv:2406.01867},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01867},
    primaryClass={cs.CV}
}