Temel Kavramlar
The author proposes integrating human-centric priors directly into the model fine-tuning stage to improve human image generation without extra conditions at inference. By introducing a Human-centric Alignment loss and scale-aware constraints, the method enhances structural accuracy and detail richness in generated images.
Özet
The content explores improving human image generation by integrating human-centric priors directly into diffusion models. The proposed method addresses challenges in anatomical accuracy and structural integrity by leveraging cross-attention maps and specialized alignment losses. Extensive experiments demonstrate significant improvements over existing state-of-the-art models, ensuring high-quality image synthesis based on textual prompts.
İstatistikler
Existing methods address anatomical imperfections in human images.
Proposed method introduces a Human-centric Alignment loss.
Extensive experiments show improvements over state-of-the-art models.
Integration of human-centric priors enhances structural accuracy.
Alıntılar
"Extensive experiments show that our method largely improves over state-of-the-art text-to-image models."
"Our approach adopts a step and scale aware training strategy to balance structural accuracy and detail richness."