Human Motion Unlearning
Authors: name: Edoardo De Matteis, name: Matteo Migliarini, name: Alessio Sampieri, name: Indro Spinelli, name: Fabio Galasso

We introduce the task of human motion unlearning to prevent the synthesis of
toxic animations while preserving the general text-to-motion generative
performance. Unlearning toxic motions is challenging as those can be generated
from explicit text prompts and from implicit toxic combinations of safe motions
(e.g., ``kicking" is ``loading and swinging a leg"). We propose the first
motion unlearning benchmark by filtering toxic motions from the large and
recent text-to-motion datasets of HumanML3D and Motion-X. We propose baselines,
by adapting state-of-the-art image unlearning techniques to process
spatio-temporal signals. Finally, we propose a novel motion unlearning model
based on Latent Code Replacement, which we dub LCR. LCR is training-free and
suitable to the discrete latent spaces of state-of-the-art text-to-motion
diffusion models. LCR is simple and consistently outperforms baselines
qualitatively and quantitatively. Project page:
https://www.pinlab.org/hmu{https://www.pinlab.org/hmu}.