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

• URL: http://arxiv.org/abs/2512.12372v1
• Date: Sat, 13 Dec 2025 15:57:29 GMT
• Title: STAGE: Storyboard-Anchored Generation for Cinematic Multi-shot Narrative
• Abstract summary: We introduce a SToryboard-Anchored GEneration workflow to reformulate the STAGE-based video generation task.<n>Instead of using sparses, we propose STEP2 to predict a structural storyboard composed of start-end frame pairs for each shot.<n>We also contribute the large-scale ConStoryBoard dataset, including high-quality movie clips with fine-grained narratives for story progression, cinematic attributes, and human preferences.
• Score: 55.05324155854762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• SmartDirector: Keyframe-Conditioned Cinematic Video Generation with Narrative Pacing Control [14.692728579474169]
  We propose SmartDirector, a framework that enhances the narrative capacity of video generation models.<n>SmartDirector supports flexible generation scenarios including single-shot generation, multi-shot narrative synthesis, and video extension.
  arXiv  Detail & Related papers  (2026-05-27T03:16:09Z)
• Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration [61.98029663481308]
  Soap2Soap is a multi-agent framework that enforces long-term visual consistency through a DualBridge mechanism.<n>A closed-loop verification agent audits identity, stability, and alignment to trigger selective regeneration.
  arXiv  Detail & Related papers  (2026-05-17T12:38:21Z)
• MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation [17.275771056805556]
  MuSS is a large-scale, dual-track dataset tailored for multi-shot video and S2V generation.<n> Sourced from over 3,000 movies, MuSS explicitly supports both complex montage transitions and subject-to-paste narratives.<n>We propose the Cinematic Narrative Benchmark, featuring a visualACPlogic-centric paradigm and a novel Anti-Copy-Paste Variance metric.
  arXiv  Detail & Related papers  (2026-04-26T16:28:46Z)
• StoryMem: Multi-shot Long Video Storytelling with Memory [32.97816766878247]
  We propose StoryMem, a paradigm that reformulates long-form video storytelling as iterative shot synthesis conditioned on explicit visual memory.<n>The proposed framework naturally accommodates smooth shot transitions and customized story generation applications.
  arXiv  Detail & Related papers  (2025-12-22T16:23:24Z)
• FilmWeaver: Weaving Consistent Multi-Shot Videos with Cache-Guided Autoregressive Diffusion [46.67733869872552]
  textbfFilmWeaver is a framework designed to generate consistent, multi-shot videos of arbitrary length.<n>Our key insight is to decouple the problem into inter-shot consistency and intra-shot coherence.<n>Our method surpasses existing approaches on metrics for both consistency and aesthetic quality.
  arXiv  Detail & Related papers  (2025-12-12T04:34:53Z)
• OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory [47.073128448877775]
  We propose OneStory, enabling global yet compact cross-shot context modeling for consistent and scalable narrative generation.<n>OneStory reformulates MSV as a next-shot generation task, enabling autoregressive shot synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning.<n>OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings.
  arXiv  Detail & Related papers  (2025-12-08T18:32:24Z)
• MultiShotMaster: A Controllable Multi-Shot Video Generation Framework [67.38203939500157]
  Current generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos.<n>We propose MultiShotMaster, a framework for highly controllable multi-shot video generation.
  arXiv  Detail & Related papers  (2025-12-02T18:59:48Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [70.61101071902596]
  Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines.<n>We introduce VideoGen-of-Thought (VGoT), a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT generates multi-shot videos that outperform state-of-the-art baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2025-03-19T11:59:14Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [76.3175166538482]
  VideoGen-of-Thought (VGoT) is a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT addresses three core challenges: Narrative fragmentation, visual inconsistency, and transition artifacts.<n>Combined in a training-free pipeline, VGoT surpasses strong baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2024-12-03T08:33:50Z)
• Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding [11.211803499867639]
  We propose DYTO, a novel dynamic token merging framework for zero-shot video understanding.<n> DYTO integrates a hierarchical frame selection and a bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences.<n>Experiments demonstrate the effectiveness of DYTO, achieving superior performance compared to both fine-tuned and training-free methods.
  arXiv  Detail & Related papers  (2024-11-21T18:30:11Z)
• StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration [88.94832383850533]
  We propose a multi-agent framework designed for Customized Storytelling Video Generation (CSVG) StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process. Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency. Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.
  arXiv  Detail & Related papers  (2024-11-07T18:00:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.