Concepts de base
Diffusion Noise Optimization (DNO) is a simple yet effective approach that can leverage existing motion diffusion models as universal motion priors to enable a wide range of motion-related tasks, including editing, completion, and refinement, without the need for model retraining.
Résumé
The paper proposes 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.
Key highlights:
- DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model, allowing it to support any use cases where criteria can be defined as a function of motion.
- For motion editing, DNO outperforms existing methods in both achieving the objective and preserving the motion content. It accommodates a diverse range of editing modes, including changing trajectory, pose, joint locations, or avoiding newly added obstacles.
- For motion denoising and completion, DNO produces 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.
- The authors conduct extensive experiments to validate the design choices of DNO and demonstrate its effectiveness on a wide range of motion-related tasks.
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Citations
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