Story2Board: A Training-Free Approach for Expressive Storyboard
  Generation
Authors: name: David Dinkevich, name: Matan Levy, name: Omri Avrahami, name: Dvir Samuel, name: Dani Lischinski

We present Story2Board, a training-free framework for expressive storyboard
generation from natural language. Existing methods narrowly focus on subject
identity, overlooking key aspects of visual storytelling such as spatial
composition, background evolution, and narrative pacing. To address this, we
introduce a lightweight consistency framework composed of two components:
Latent Panel Anchoring, which preserves a shared character reference across
panels, and Reciprocal Attention Value Mixing, which softly blends visual
features between token pairs with strong reciprocal attention. Together, these
mechanisms enhance coherence without architectural changes or fine-tuning,
enabling state-of-the-art diffusion models to generate visually diverse yet
consistent storyboards. To structure generation, we use an off-the-shelf
language model to convert free-form stories into grounded panel-level prompts.
To evaluate, we propose the Rich Storyboard Benchmark, a suite of open-domain
narratives designed to assess layout diversity and background-grounded
storytelling, in addition to consistency. We also introduce a new Scene
Diversity metric that quantifies spatial and pose variation across storyboards.
Our qualitative and quantitative results, as well as a user study, show that
Story2Board produces more dynamic, coherent, and narratively engaging
storyboards than existing baselines.