cs.CVApr 1, 2026

StoryBlender: Inter-Shot Consistent and Editable 3D Storyboard with Spatial-temporal Dynamics

Authors: Bingliang Li, Zhenhong Sun, Jiaming Bian, Yuehao Wu, Yifu Wang, Hongdong Li, Yatao Bian, Huadong Mo, +1 more

Organizations: Independent Researcher · Australia National University Australia · Central South University China · University of New South Wales Australia · Vertex Lab China · National University of Singapore Singapore · University of Technology Sydney Australia

Abstract

Storyboarding is a core skill in visual storytelling for film, animation, and games. However, automating this process requires a system to achieve two properties that current approaches rarely satisfy simultaneously: inter-shot consistency and explicit editability. While 2D diffusion-based generators produce vivid imagery, they often suffer from identity drift along with limited geometric control; conversely, traditional 3D animation workflows are consistent and editable but require expert-heavy, labor-intensive authoring. We present StoryBlender, a grounded 3D storyboard generation framework governed by a Story-centric Reflection Scheme. At its core, we propose the StoryBlender system, which is built on a three-stage pipeline: (1) Semantic-Spatial Grounding, to construct a continuity memory graph to decouple global assets from shot-specific variables for long-horizon consistency; (2) Canonical Asset Materialization, to instantiate entities in a unified coordinate space to maintain visual identity; and (3) Spatial-Temporal Dynamics, to achieve layout design and cinematic evolution through visual metrics. By orchestrating multiple agents in a hierarchical manner within a verification loop, StoryBlender iteratively self-corrects spatial hallucinations via engine-verified feedback. The resulting native 3D scenes support direct, precise editing of cameras and visual assets while preserving unwavering multi-shot continuity. Experiments demonstrate that StoryBlender significantly improves consistency and editability over both diffusion-based and 3D-grounded baselines. Code, data, and demonstration video are available on https://engineeringai-lab.github.io/StoryBlender/

Explore similar work

CardsList
  1. DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

    Apr 19, 2026Junjia Huang, Binbin Yang, Pengxiang Yan +6Interactive Video GenerationVideo Diffusion Models

  2. S2ED: From Story to Executable Descriptions for Consistency-Aware Story Illustration

    May 21, 2026Sijing Yin, Jiamou Liu, Xiao Tang +2NarrativesText-To-Image

  3. StoryEngine: A State-Grounded Agentic Framework for Video Storytelling

    Sep 27, 2026Yingrui Wang, Zeqing Wang, Yeying JinVideo StorytellingNarratives