DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation
Diffusion-based generative AI has achieved remarkable success in e-commerce applications such as virtual try-on, poster generation, and product background synthesis. However, when making online purchasing decisions for apparel, consumers also desire the freedom to examine specific detail regions of interest, such as collars, cuffs, and fabric textures, yet existing methods have not explicitly studied this setting. We therefore formalize a new, non-template task: Fashion Detail Generation with focus conditioning, and release FDBench, the first benchmark comprising 40K+ human-verified reference-
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Zijun Li →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Yimin Zhou →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Jia Sun →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Honglie Wang →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Pengcheng Wei →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Junlong Wu →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Yongrui Heng →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”
- LinkedLinked via arxiv author · 85%Jiyuan Wang →
“DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation”