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Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling

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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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arXiv Computer Vision
2d ago

Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling

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.

By SeungJu Cha, Ye-Chan Kim, Kwanyoung Lee, Dong-Jin Kim
arXiv AI
Jul 8

Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation

arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.

By Anna C\'ordoba, Adam Puente Tercero, Nerea Angulo Hijo, Mar Linares Tercero, Julia Barrientos, Ainhoa Miranda, Jes\'us Olivera