New CoFi method improves long-horizon generation with diffusion models

Researchers have developed a new method called Coarse-to-Fine Compositional Diffusion (CoFi) to improve the generation of long-horizon outputs from diffusion models. CoFi separates the process into two stages: first, it forms a global structure by aligning local plans, and then it refines this structure with local details. This approach enhances both global coherence and local sample quality across various applications like robotic planning and video generation, while also reducing the number of required denoiser evaluations. AI

IMPACT Enhances long-horizon generative tasks by improving coherence and efficiency.

RANK_REASON The cluster contains an academic paper detailing a new method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Coarse-to-Fine Compositional Diffusion
• Diffusion models
• Long video generation
• Panoramic image generation
• Robotic planning

• paper
• other

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