title: TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
summary: The gradual nature of a diffusion process that synthesizes samples in small
  increments constitutes a key ingredient of Denoising Diffusion Probabilistic
  Models (DDPM), which have presented unprecedented quality in image synthesis
  and been recently explored in the motion domain. In this work, we propose to
  adapt the gradual diffusion concept (operating along a diffusion time-axis)
  into the temporal-axis of the motion sequence. Our key idea is to extend the
  DDPM framework to support temporally varying denoising, thereby entangling the
  two axes. Using our special formulation, we iteratively denoise a motion buffer
  that contains a set of increasingly-noised poses, which auto-regressively
  produces an arbitrarily long stream of frames. With a stationary diffusion
  time-axis, in each diffusion step we increment only the temporal-axis of the
  motion such that the framework produces a new, clean frame which is removed
  from the beginning of the buffer, followed by a newly drawn noise vector that
  is appended to it. This new mechanism paves the way towards a new framework for
  long-term motion synthesis with applications to character animation and other
  domains.
paper_authors._id: 64c34ee97f9b6ae10e140367
paper_authors.name: Zihan Zhang
paper_authors._id: 64c34ee97f9b6ae10e140368
paper_authors.name: Richard Liu
paper_authors._id: 64c34ee97f9b6ae10e140369
paper_authors.name: Kfir Aberman
paper_authors._id: 64c34ee97f9b6ae10e14036a
paper_authors.name: Rana Hanocka
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-28T09:36:12.018Z
paper_authors._id: 630495ffce6b12280b193f30
paper_authors.avatarUrl: /avatars/55b201131fa798c2a33d17dcab72fd1f.svg
paper_authors.fullname: Rana Hanocka
paper_authors.type: user
paper_authors.user: ranahanocka
paper_title: TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
paper_summary: The gradual nature of a diffusion process that synthesizes samples in small
  increments constitutes a key ingredient of Denoising Diffusion Probabilistic
  Models (DDPM), which have presented unprecedented quality in image synthesis
  and been recently explored in the motion domain. In this work, we propose to
  adapt the gradual diffusion concept (operating along a diffusion time-axis)
  into the temporal-axis of the motion sequence. Our key idea is to extend the
  DDPM framework to support temporally varying denoising, thereby entangling the
  two axes. Using our special formulation, we iteratively denoise a motion buffer
  that contains a set of increasingly-noised poses, which auto-regressively
  produces an arbitrarily long stream of frames. With a stationary diffusion
  time-axis, in each diffusion step we increment only the temporal-axis of the
  motion such that the framework produces a new, clean frame which is removed
  from the beginning of the buffer, followed by a newly drawn noise vector that
  is appended to it. This new mechanism paves the way towards a new framework for
  long-term motion synthesis with applications to character animation and other
  domains.
paper_ai_summary: The work adapts DDPM for motion synthesis by extending the diffusion framework to a temporal axis, enabling the generation of long streams of clean motion frames.
paper_ai_keywords: Denoising Diffusion Probabilistic Models DDPM diffusion process image synthesis motion domain temporal-axis temporally varying denoising motion buffer clean frame long-term motion synthesis character animation