  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.
