ResearchResearch paperMultimodal Models · Image, Video & 3D Generation · Large Language Models1 source · Oct 15, 2026

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

We propose EOS-Story, a training-free framework that harmonizes identity consistency with scene-specific adaptation across text and vision spaces.

Recent advancements in diffusion models have significantly improved text-to-image generation, enabling their application to visual storytelling, where coherent image sequences are generated from narrative prompts.
To bridge this gap, we introduce Cross-Contextual Bridging, which softly injects a phrase-level appearance residual into the scene's <EOS> embedding.
In the vision space, we find that scale mismatch between identity and scene subjects causes a few identity tokens to be repeatedly transferred, producing structural artifacts.
We address this with Adaptive Identity Preservation, which anchors identity features at the scene-specific scale (Identity Anchoring) and integrates them via Identity-Aware Self-Attention.

[1]Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:51 PM
We propose EOS-Story, a training-free framework that harmonizes identity consistency with scene-specific adaptation across text and vision spaces.
Recent advancements in diffusion models have significantly improved text-to-image generation, enabling their application to visual storytelling, where coherent image sequences are generated from narrative prompts.

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