Coarse-Guided Visual Generation via Weighted h-Transform Sampling
📰 ArXiv cs.AI
Coarse-guided visual generation uses weighted h-Transform sampling for fine visual sample synthesis from low-fidelity references
Action Steps
- Leverage pretrained diffusion models for guidance
- Incorporate weighted h-Transform sampling for coarse-guided visual generation
- Fine-tune the model for specific applications to improve generalization
- Evaluate the generated visuals for quality and coherence
Who Needs to Know This
AI researchers and engineers working on computer vision and image generation tasks can benefit from this approach to improve the quality of generated visuals, and product managers can apply this to various real-world applications
Key Insight
💡 Weighted h-Transform sampling can improve the quality of generated visuals from low-fidelity references
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💡 Coarse-guided visual generation via weighted h-Transform sampling
Key Takeaways
Coarse-guided visual generation uses weighted h-Transform sampling for fine visual sample synthesis from low-fidelity references
Full Article
Title: Coarse-Guided Visual Generation via Weighted h-Transform Sampling
Abstract:
arXiv:2603.12057v2 Announce Type: replace-cross Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance
Abstract:
arXiv:2603.12057v2 Announce Type: replace-cross Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance
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