# STAGE: Storyboard-Anchored Generation for Cinematic Multi-shot Narrative

arXiv: https://arxiv.org/abs/2512.12372 · HTML: https://mlsanity.com/paper/2512.12372/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2025.

## Built on by (most cited first, 23 of 23)

- [Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning](https://mlsanity.com/paper/2601.02902/) (2026)
- [ViMax: Agentic Video Generation](https://mlsanity.com/paper/2606.07649/) (2026)
- [A Benchmark and Multi-Agent System for Instruction-driven Cinematic Video Compilation](https://mlsanity.com/paper/2604.10456/) (2026)
- [CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation](https://mlsanity.com/paper/2606.09639/) (2026)
- [UnityShots: Memory-Driven Multi-Shot Audio-Video Generation with Boundary-Aware Gating](https://mlsanity.com/paper/2606.21661/) (2026)
- [Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.02993/) (2026)
- [GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling](https://mlsanity.com/paper/2606.20799/) (2026)
- [MTAVG-Bench 2.0: Diagnosing Failure Modes of Cinematic Expressiveness in Multi-Talker Audio-Video Generation](https://mlsanity.com/paper/2605.28035/) (2026)
- [CANVAS: Continuity-Aware Narratives via Visual Agentic Storyboarding](https://mlsanity.com/paper/2604.13452/) (2026)
- [RECAP-Forcing: Retaining Content Appearances for Long Video Generation](https://mlsanity.com/paper/2608.26671/) (2026)
- [EvoState: Closed-Loop Visual State Management for Long-Form Video Generation](https://mlsanity.com/paper/2606.16184/) (2026)
- [DramaDirector: Geometry-Guided Short Drama Generation](https://mlsanity.com/paper/2606.24107/) (2026)
- [FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling](https://mlsanity.com/paper/2607.19038/) (2026)
- [LogiShot: Logically Coherent Cross-Shot Video Generation](https://mlsanity.com/paper/2608.08820/) (2026)
- [SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale](https://mlsanity.com/paper/2608.22725/) (2026)
- [ReContraster: Making Your Posters Stand Out with Regional Contrast](https://mlsanity.com/paper/2604.10442/) (2026)
- [DrawVideo: Grounded and Faithful Multi-Shot Video Generation from Storyboard Keyframe Sketches](https://mlsanity.com/paper/2605.23508/) (2026)
- [V2V-Bench: A Comprehensive Benchmark for Video-to-Video Generation Evaluation](https://mlsanity.com/paper/2606.05665/) (2026)
- [MAVIN: Multi-Shot Audio-Visual Generation with Customized Narrative Control](https://mlsanity.com/paper/2606.29473/) (2026)
- [PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation](https://mlsanity.com/paper/2608.16717/) (2026)
- [Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation](https://mlsanity.com/paper/2609.06373/) (2026)
- [PACE: Precise AI Cinematic Expression](https://mlsanity.com/paper/2609.19853/) (2026)
- [StoryEngine: A State-Grounded Agentic Framework for Video Storytelling](https://mlsanity.com/paper/2609.33627/) (2026)

## Abstract (arXiv)

While recent advancements in generative models have achieved remarkable visual fidelity in video synthesis, creating coherent multi-shot narratives remains a significant challenge. To address this, keyframe-based approaches have emerged as a promising alternative to computationally intensive end-to-end methods, offering the advantages of fine-grained control and greater efficiency. However, these methods often fail to maintain cross-shot consistency and capture cinematic language. In this paper, we introduce STAGE, a SToryboard-Anchored GEneration workflow to reformulate the keyframe-based multi-shot video generation task. Instead of using sparse keyframes, we propose STEP2 to predict a structural storyboard composed of start-end frame pairs for each shot. We introduce the multi-shot memory pack to ensure long-range entity consistency, the dual-encoding strategy for intra-shot coherence, and the two-stage training scheme to learn cinematic inter-shot transition. We also contribute the large-scale ConStoryBoard dataset, including high-quality movie clips with fine-grained annotations for story progression, cinematic attributes, and human preferences. Extensive experiments demonstrate that STAGE achieves superior performance in structured narrative control and cross-shot coherence. Our code will be available at this url.