title: Human Motion Unlearning
summary: 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}.
paper_authors._id: 67e2c6f567aea67078c6a5f0
paper_authors.name: Edoardo De Matteis
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T19:01:57.585Z
paper_authors._id: 641af8477c21ab946bf18a91
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/641af8477c21ab946bf18a91/iuJpnsvfMEPP-YWcXyh1D.jpeg
paper_authors.fullname: Edoardo De Matteis
paper_authors.type: user
paper_authors.user: edodema
paper_authors._id: 67e2c6f567aea67078c6a5f1
paper_authors.name: Matteo Migliarini
paper_authors._id: 67e2c6f567aea67078c6a5f2
paper_authors.name: Alessio Sampieri
paper_authors._id: 67e2c6f567aea67078c6a5f3
paper_authors.name: Indro Spinelli
paper_authors._id: 67e2c6f567aea67078c6a5f4
paper_authors.name: Fabio Galasso
paper_title: Human Motion Unlearning
paper_summary: 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}.
paper_ai_summary: 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.
paper_ai_keywords: human motion unlearning text-to-motion HumanML3D Motion-X spatio-temporal signals Latent Code Replacement LCR text-to-motion diffusion models