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

arXiv cs.CV / 4/7/2026

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Key Points

  • StoryBlender is a proposed grounded 3D storyboard generation framework aimed at simultaneously improving inter-shot visual consistency and explicit editability, which existing 2D diffusion and traditional 3D workflows struggle with.
  • The system uses a three-stage pipeline—Semantic-Spatial Grounding, Canonical Asset Materialization, and Spatial-Temporal Dynamics—to maintain identity across shots and to control both spatial layout and cinematic evolution.
  • StoryBlender employs a hierarchical multi-agent approach with a verification loop that uses engine-verified feedback to self-correct spatial hallucinations over iterations.
  • The resulting output is native 3D scene data designed for direct, precise editing of cameras and assets while preserving multi-shot continuity.
  • The authors report that experiments show significantly better consistency and editability versus diffusion-based and other 3D-grounded baselines, with code/data/video planned for release on the project site.

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 will be available on https://engineeringai-lab.github.io/StoryBlender/