Optimizing Diffusion Noise Can Serve As Universal Motion Priors
METADATA ONLY
Loading...
Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
External links
Editor
Book title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
1334 - 1345
Publisher
IEEE
Event
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
motion generation; diffusion model; diffusion noise optimization; conditional generation
Organisational unit
09686 - Tang, Siyu / Tang, Siyu
Notes
Funding
204840 - Learning to Create Realistic Human Avatars (SNF)