TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
This work addresses the challenge of long-term motion synthesis for domains such as character animation, presenting a novel framework that is incremental in adapting existing diffusion concepts.
The paper tackles the problem of generating long-term motion sequences by adapting diffusion models to operate along both diffusion and temporal axes, enabling auto-regressive synthesis of arbitrarily long motion streams for applications like character animation.
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.