ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
EOS-Story is a training‑free framework that improves visual storytelling by balancing identity consistency with scene‑specific adaptation. It separates narrative prompts into identity and scene sub‑prompts and introduces Cross‑Contextual Bridging to inject appearance cues into the scene embedding. In the vision space, Adaptive Identity Preservation aligns identity features to the scene scale and uses Identity‑Aware Self‑Attention to avoid structural artifacts, achieving competitive results on the extended Consistory+ benchmark.
EOS-Story is a training‑free framework that improves visual storytelling by balancing identity consistency with scene‑specific adaptation. It separates narrative prompts into identity and scene sub‑prompts, then uses Cross‑Contextual Bridging to inject appearance cues into the scene embedding. In the vision space, Adaptive Identity Preservation aligns identity features to the scene scale and employs Identity‑Aware Self‑Attention to avoid structural artifacts, achieving strong results on the Consistory+_+ benchmark.
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