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

Paper recorded by Signals 4 on 2026-10-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.CV · 计算机视觉 · first seen 2026-10-16

Abstract

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. However, existing methods primarily focus on identity preservation and often fail to adapt to diverse scene variations, resulting in semantic misalignment and structural inflexibility. W

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#258 most recent of 756 cs.CV papers we have recorded · ↑ newer: Dense4D: Pixel-Level 4D Human Correspondence · ↓ older: Velox: Spatial Memory Makes World Models Efficient
Cite this page: Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling: the #258 most recent of 756 cs.CV papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/not-just-a-subject-capturing-adaptive-scene-specific-variance-for-visual-storyte.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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