TEDi：用于长期运动合成的时间纠缠扩散

摘要

在噪声扩散概率模型（DDPM）中，以小增量合成样本的扩散过程的渐进性质构成了关键要素，这在图像合成方面表现出前所未有的质量，并最近在运动领域得到了探索。在这项工作中，我们提议将渐进扩散概念（沿扩散时间轴操作）调整到运动序列的时间轴上。我们的关键思想是将DDPM框架扩展到支持时间变化的去噪，从而将这两个轴纠缠在一起。利用我们的特殊公式，我们迭代地去噪一个包含一组逐渐加噪姿势的运动缓冲区，这个过程自回归地生成任意长度的帧流。在固定的扩散时间轴上，在每个扩散步骤中，我们仅增加运动的时间轴，使框架生成一个新的干净帧，该帧从缓冲区的开头移除，然后附加一个新绘制的噪声向量。这种新机制为长期运动合成的新框架铺平了道路，适用于角色动画和其他领域。

English

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.

TEDi：用于长期运动合成的时间纠缠扩散

TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis

摘要

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