Hypernetwork-Driven LoRA for Stylized Motion Generation
A novel, lightweight framework has been developed for generating stylized text-to-motion, utilizing LoRA parameters created by a hypernetwork to adjust a pretrained diffusion model in real-time. This technique encodes a reference motion style into a global embedding, which the hypernetwork translates into low-rank updates implemented during each denoising phase. The style latent space is organized through a supervised contrastive loss. By circumventing the need for style-specific fine-tuning and complex ControlNet architectures, this method enhances both efficiency and the ability to generalize to new styles.
Key facts
- arXiv:2605.13333v1
- Hypernetwork-generated LoRA parameters
- Style reference motion encoded into global style embedding
- Low-rank updates applied at each denoising step
- Supervised contrastive loss structures style latent space
- Avoids style-specific fine-tuning
- Avoids heavy ControlNet architectures
- Improves efficiency and generalization to unseen styles
Entities
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