Human Motion Unlearning
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}.
67e2c6f567aea67078c6a5f0
Edoardo De Matteis
claimed_verified
2025-03-25T19:01:57.585Z
641af8477c21ab946bf18a91
https://cdn-avatars.huggingface.co/v1/production/uploads/641af8477c21ab946bf18a91/iuJpnsvfMEPP-YWcXyh1D.jpeg
Edoardo De Matteis
user
edodema
67e2c6f567aea67078c6a5f1
Matteo Migliarini
67e2c6f567aea67078c6a5f2
Alessio Sampieri
67e2c6f567aea67078c6a5f3
Indro Spinelli
67e2c6f567aea67078c6a5f4
Fabio Galasso
Human Motion Unlearning
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}.
A new benchmark and model are introduced for unlearning toxic motions in text-to-motion synthesis, using Latent Code Replacement to effectively remove undesired motions while maintaining overall performance.
human motion unlearning text-to-motion HumanML3D Motion-X spatio-temporal signals Latent Code Replacement LCR text-to-motion diffusion models