We propose Diffusion Noise Optimization (DNO), a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. Instead of training a task-specific diffusion model for each new task, DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model. Given the corresponding latent noise of a human motion, it propagates the gradient from the target criteria defined on the motion space through the whole denoising process to update the diffusion latent noise. As a result, DNO supports any use cases where criteria can be defined as a function of motion. In particular, we show that, for motion editing and control, DNO outperforms existing methods in both achieving the objective and preserving the motion content. DNO accommodates a diverse range of editing modes, including changing trajectory, pose, joint locations, or avoiding newly added obstacles. In addition, DNO is effective in motion denoising and completion, producing smooth and realistic motion from noisy and partial inputs. DNO achieves these results at inference time without the need for model retraining, offering great versatility for any defined reward or loss function on the motion representation.

我们提出了扩散噪声优化（DNO），一种新的方法，通过有效利用现有的运动扩散模型作为广泛运动相关任务的运动先验，而无需为每个新任务训练特定于任务的扩散模型。DNO支持任何可将准则定义为运动函数的用例，并在运动编辑和控制以及运动去噪和补全方面优于现有方法，同时使其具有适应多种编辑模式的能力，包括改变轨迹、姿势、关节位置或避开新添加的障碍物，并能在推理时间内实现这些结果而无需模型重训练，为运动表示的任何定义的奖励或损失函数提供了很大的灵活性。

优化扩散噪声可作为通用运动先验