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VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention

Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma, Yexin Liu, Wenjie Shu, Yatian Pang, Feilong Tang, Qifeng Chen, Harry Yang, Ser-Nam Lim

arXiv:2412.02259v3cs.CVcs.AI

TL;DR

Multi-shot video generation remains challenging because systems must sustain reasonable storylines and visual consistency across scenes, including character identity. VGoT generates multi-shot videos from one sentence through storyline modeling, identity-aware propagation, and boundary-aware latent processing. It reports improved consistency over prior methods and fewer manual adjustments.

  • Problem

    Generating multi-shot videos from brief prompts requires reasonable storylines and visual consistency across shots, including preserving identity while allowing story-driven trait changes.

    Source: §1 · p. 2

  • Method

    VGoT uses dynamic storyline modeling, identity-aware cross-shot propagation, and adjacent-shot latent reset mechanisms in a single-sentence-to-video pipeline.

    Source: §1 · p. 2, §3.1 · p. 5

  • Results

    20.39% higher within-shot face consistency and 17.36% better style consistency than previous state-of-the-art methods, with 10× fewer manual adjustments than alternatives.

    Source: §6 · p. 9

  • Takeaways & Limitations

    The authors position VGoT as bridging raw visual synthesis with cinematic storytelling while maintaining director-level narrative flow.

    Source: §6 · p. 9

    VGoT relies on pretrained components without additional finetuning, so performance is bounded by base-model capabilities, including motion diversity under complex camera trajectories.

    Source: A Limitations, Licenses, and Future Work · p. 14

Abstract

from arXiv · show

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutions either rely on extensive manual scripting/editing or prioritize single-shot fidelity over cross-scene continuity, limiting their practicality for movie-like content. We introduce VideoGen-of-Thought (VGoT), a step-by-step framework that automates multi-shot video synthesis from a single sentence by systematically addressing three core challenges: (1) Narrative fragmentation: Existing methods lack structured storytelling. We propose dynamic storyline modeling, which turns the user prompt into concise shot drafts and then expands them into detailed specifications across five domains (character dynamics, background continuity, relationship evolution, camera movements, and HDR lighting) with self-validation to ensure logical progress. (2) Visual inconsistency: previous approaches struggle to maintain consistent appearance across shots. Our identity-aware cross-shot propagation builds identity-preserving portrait (IPP) tokens that keep character identity while allowing controlled trait changes (expressions, aging) required by the story. (3) Transition artifacts: Abrupt shot changes disrupt immersion. Our adjacent latent transition mechanisms implement boundary-aware reset strategies that process adjacent shots' features at transition points, enabling seamless visual flow while preserving narrative continuity. Combined in a training-free pipeline, VGoT surpasses strong baselines by 20.4\% in within-shot face consistency and 17.4\% in style consistency, while requiring 10x fewer manual adjustments. VGoT bridges the gap between raw visual synthesis and director-level storytelling for automated multi-shot video generation.

1 Introduction

Multi-shot generation must keep storylines and visual elements coherent across shots, including preserving character identity while allowing story-driven trait changes. VGoT automates generation from a single sentence with a structured pipeline and introduces an evaluation protocol for within-shot and cross-shot consistency.

1 Introduction

  • VGoT generates story-coherent multi-shot videos from one sentence with minimal manual intervention, addressing narrative fragmentation, visual inconsistency, and transition artifacts.Its pipeline combines dynamic storyline modeling, identity-aware cross-shot propagation, and adjacent latent transition mechanisms.Source: §1 · pp. 2–3
  • Dynamic storyline modeling converts prompts into shot sequences and uses self-validation to reject candidates that violate cinematic principles.This is intended to produce a reasonable story draft across shots.Source: §1 · p. 2
  • Identity-aware propagation derives portrait schemata from narrative elements, supporting different avatars and trait changes for the same identity across a story.The resulting identity-preserving portrait tokens guide keyframe generation through hierarchical feature injection in pretrained diffusion models.Source: §1 · pp. 2–3
  • A FIFO-like latent reset processes adjacent-shot features at boundaries to support seamless transitions and visual continuity while preserving storyline logic.Shot video latents are refined from keyframe and textual latents.Source: §1 · p. 3
  • The proposed assessment protocol uses four metrics to measure face and style consistency within shots and across shots.The metrics cover within-shot face and style consistency and cross-shot face and style consistency.Source: §1 · p. 3
  • 20.4% and 17.4% improvements in WS-FC and WS-SC, respectively, plus 2.9× higher CS-FC and 106.6% higher CS-SC over baselines.Human evaluations report 66.7% “Good” ratings for cross-shot consistency versus 27.2% for competitors.Source: §1 · p. 3

2 Related Work

Related work distinguishes long-video synthesis from multi-shot story generation, with both lines facing challenges in coherence or automation. VGoT combines story decomposition, identity propagation, and boundary-aware latent processing to address these issues.

Long Video Synthesis

  • Long-video methods can achieve visual fidelity but struggle with coherence over extended sequences; autoregressive methods also face error accumulation and computational expense.The related-work discussion contrasts diffusion approaches with autoregressive frame prediction and notes that training-free methods lack mechanisms for shot transitions.Source: §2 · pp. 3–4

Multi-Shot Video Generation

  • Prior multi-shot methods face automation limits, including extensive scripting or image curation, repeated adjustments, and difficulty maintaining logical transitions.Animate-a-Story (He et al. (2023)) is described as struggling with logical narrative transitions, while MovieDreamer (Zhao et al. (2024)) requires manual scripting and image curation.Source: §2 · p. 4
  • VGoT organizes multi-shot generation around LLM-driven story decomposition, story-derived identity propagation, and boundary-aware latent processing.The cited comparison reports 10× fewer manual interventions than prior works (Zhao et al. (2024); Xie et al. (2024)).Source: §2 · p. 4, Figure 2 · p. 4

3 Preliminaries and Problem Formulation

The section outlines diffusion-based video generation and frames VGoT’s task as producing coherent, identity-consistent multi-shot videos from one sentence. Its problem formulation emphasizes narrative flow, identity preservation with trait variation, and connected shots.

3.1 Preliminaries

  • Diffusion models iteratively denoise random noise, with a forward process adding noise and a learned reverse process reconstructing the original data.Training minimizes noise-prediction error.Source: §3.1 · pp. 4–5
  • Latent diffusion performs this process in compressed space, while video diffusion processes frame sequences through temporal-aware denoising networks.Text conditioning uses cross-attention; FIFO-Diffusion adds queue-based latent processing.Source: §3.1 · p. 5
  • FIFO-Diffusion supports frame continuity but struggles with abrupt shot transitions and image-based conditions; VGoT addresses these with adjacent latent-space resets.The cited passage identifies these as limitations of the FIFO approach.Source: §3.1 · p. 5

3.2 Problem Definition

  • The task is to generate a visually consistent, reasonably plotted multi-shot video from a one-sentence input specifying N shots, with minimal manual intervention.Source: §3.2 · p. 5
  • Reasonability requires maintaining logical narrative flow as the storyline evolves.Source: §3.2 · p. 5
  • Consistency requires preserving high-level identity while allowing shot-specific trait changes such as aging, expressions, and contextual relationships.Source: §3.2 · p. 5
  • The generation task also calls for minute-level videos composed of diverse but interconnected shots.Source: §3.2 · p. 5
  • VGoT addresses the formulation through script preparation and narrative-visual coherence mechanisms.Source: §3.2 · p. 5

4 Method: VideoGen-of-Thought

VGoT is a structured, step-by-step framework for generating multi-shot videos from one sentence. It combines self-validated storyline construction, identity-aware keyframes, and boundary-aware latent resets to support narrative and visual continuity.

4 Method: VideoGen-of-Thought

  • The framework organizes single-sentence multi-shot generation into four collaborative modules that address narrative fragmentation, visual inconsistency, and transition artifacts.Source: §4 · p. 5

4.1 Dynamic Storyline Modeling with Self-Vaildation

  • Dynamic storyline modeling drafts short shot descriptions, expands them into structured scripts, and self-validates candidates for narrative coherence and cinematic constraints.Scripts specify character evolution and relationships, background continuity, interaction patterns and event causality, camera movement, and HDR lighting; candidates violating the criteria are rejected.Source: §4.1 · pp. 5–6

4.2 Identity-Aware Cross-Shot Propagation

  • Identity-aware cross-shot propagation preserves critical attributes such as hairstyle and facial structure while allowing narrative-driven changes such as expression and aging.Narrative-derived identity descriptors form IPP tokens, which are injected through attention to support identity fidelity across shots.Source: §4.2 · pp. 6–7

4.3 Adjacent Latent Transition Mechanisms

  • For each shot, the transition pipeline uses a simplified shot description, keyframe embedding, and initial noise to generate shot-wise latents with boundary-aware noise reset.Source: §4.3 · p. 7
  • Table 1 compares state-of-the-art text-to-video baselines using average CLIP scores and within- and across-shot face-consistency and style-consistency scores.Source: Table 1 · p. 8
  • At shot boundaries, reset operations manage latent noise to preserve narrative flow and visual continuity without additional training.The reset function uses a noise magnitude controlled at boundaries; the resulting shot latents are decoded into the video.Source: §4.3 · pp. 7–8

5 Experiments

Experiments compare VGoT with text-to-video baselines on multi-shot narratives and examine the effects of removing Dynamic Storyline Modeling (DSM) and Identity-Preserving Portraits (IPP). Human ratings and ablations highlight cross-shot consistency and the trade-offs between consistency, narrative diversity, and visual quality.

5.1 Experiment Settings

  • The evaluation used ten 30-shot stories (300 shots total), comparing VGoT with EasyAnimate, CogVideo, and VideoCrafter.The stories were generated from single user inputs; scripting used GPT-4o, keyframes used Kolor, and video generation used DynamiCrafter.Source: §5.1 · p. 8

5.3 Ablation Studies

  • The full model scored 0.1111 on CLIP, versus 0.1223 for the baseline, while achieving its highest PSNR (25.79) and IS (7.52).The authors describe this as a trade-off between literal prompt matching and cinematic quality.Source: §5.3 · p. 9
  • Removing DSM raised ΩCS-FC by 20.2% (0.3291 vs 0.2738) and SC by 8.5% (0.4186 vs 0.3859), but produced repetitive scenes and identical camera angles.The reported consistency gains coincided with severe narrative-diversity loss.Source: §5.3 · p. 9
  • IPP alone reached 0.8745 ΩWS-FC, 5.3% above the full model, while DSM+IPP achieved 8.3% better ΩCS-SC than IPP alone and higher PSNR (25.79 vs 23.92).The authors conclude that DSM supports story progression while IPP supports continuity.Source: §5.3 · p. 9

6 Conclusion

VideoGen-of-Thought (VGoT) automates multi-shot video generation from one sentence using storyline modeling, identity-aware propagation, and latent transitions. It reports improved consistency over prior methods with fewer manual adjustments.

6 Conclusion

  • VGoT reports 20.39% higher within-shot face consistency and 17.36% better style consistency than previous state-of-the-art methods, with 10× fewer manual adjustments.The paper describes the framework as producing reasonable storylines and visual consistency from one sentence.Source: §6 · p. 9

A Limitations, Licenses, and Future Work

The paper notes that VGoT’s training-free design leaves performance bounded by pretrained components and outlines several areas for future development. It also illustrates how a single-sentence story prompt is expanded into shot descriptions and structured generation prompts.

A Limitations, Licenses, and Future Work

  • Because VGoT uses pretrained components without finetuning, its performance is bounded by their capabilities; DynamiCrafter may limit complex-camera motion, rapid appearance changes, and dependencies beyond adjacent shots.The authors identify these as limitations of training-free pipelines.Source: A Limitations, Licenses, and Future Work · p. 14
  • Planned extensions include stronger video backbones or optional finetuning, multisubject identity handling, broader cultural and linguistic coverage, and screenplay writing.The paper frames these as future work.Source: A Limitations, Licenses, and Future Work · p. 14

B Detailed Example of Dynamic Storylines

  • Dynamic Storyline Modeling expands one user sentence into a structured description and then into per-shot prompts, providing a narrative backbone for keyframe and shot generation.The process uses an LLM and prompt engineering to support a logical, stepwise narrative.Source: B Detailed Example of Dynamic Storylines · p. 14
  • In the example, one adventure prompt becomes 30 one-sentence scripts, then shot prompts specifying character, background, relation, camera pose, and HDR lighting.The example’s first shot has Mike examine a map with Jane in a misty jungle setting.Source: B Detailed Example of Dynamic Storylines · p. 14

C Multi-Shot Evaluation Protocol

The protocol addresses multi-shot evaluation’s emphasis on single-shot quality by measuring both within-shot and cross-shot consistency. It combines face- and style-consistency measures and presents them as a framework for local and global assessment.

C Multi-Shot Evaluation Protocol

  • The protocol introduces a hierarchy for measuring both intra-shot and inter-shot consistency, addressing multi-shot evaluation’s underspecification.The four measures cover within-shot and cross-shot face and style consistency.Source: C Multi-Shot Evaluation Protocol · p. 15
  • Within-shot face consistency measures identity preservation over a shot’s temporal sequence, while cross-shot face consistency evaluates identity preservation across shots.The cross-shot measure compares the last eight frames of one shot with the first eight frames of the next in this implementation.Source: C Multi-Shot Evaluation Protocol · p. 15
  • The protocol also measures within-shot stylistic coherence and style preservation between adjacent shots using VGG-19 features.The authors describe the metrics as capturing local and global consistency aspects of multi-shot video.Source: C Multi-Shot Evaluation Protocol · p. 15

D Additional Results

VGoT is demonstrated on varied multi-shot narratives and compared qualitatively with four video-generation baselines.

D Additional Results

  • The examples cover longitudinal character development, multi-actor scenes, non-human narratives, and diverse stories generated from the same input.The authors describe aging sequences, relationship continuity, and fantastical subjects among the cases.Source: D Additional Results · pp. 15–16
  • The longitudinal example follows Carlos’s journey across 30 shots.The passage begins describing a 30-shot story prompt about Carlos.Source: D Additional Results · pp. 15–16
  • VGoT is reported to outperform EasyAnimate, CogVideo, VideoCrafter1, and VideoCrafter2 in character continuity, background stability, and logical flow across shots.The comparison also considers visual consistency, narrative coherence, and overall quality.Source: D Additional Results · p. 16

E User Study

A 10-participant study compared VGoT videos with four baselines, and participants preferred VGoT, particularly for cross-shot consistency.

E User Study

  • Ten participants rated 50 accelerated multi-shot videos generated by VGoT or one of four baselines on good, normal, or bad scales.The videos came from 10 input stories of 30 shots each; each participant saw 10 videos from different sources.Source: E User Study · p. 16
  • Users significantly preferred VGoT videos, especially for cross-shot consistency, and rated them highly for narrative coherence and overall quality compared with baselines.Participants judged within-shot consistency, cross-shot consistency, and visual quality.Source: E User Study · pp. 16–17

F Ethics Statement

The ethics statement describes the study’s use of pretrained systems and identifies environmental, misuse, and cultural-representation risks alongside mitigation measures.

F Ethics Statement

  • The study used publicly available pretrained systems for script preparation, keyframes, and video synthesis, and reports that its author-created 10-story benchmark contains no personal data or copyrighted media beyond generated model outputs.The ethics statement also describes a 10-participant human evaluation, privacy protections, and compensation following local norms.Source: F Ethics Statement · pp. 17–18
  • The authors identify substantial energy use during inference and evaluation, disinformation or misleading-content misuse, and cultural bias that may reduce representation across regions or languages.The paper points to H100-class accelerators and English-centric prompts or cultural settings as examples.Source: F Ethics Statement · pp. 18–19
  • The authors recommend energy-aware configurations, content-provenance indicators, and broader cultural coverage in prompts and evaluation.Source: F Ethics Statement · p. 19

NeurIPS Paper Checklist

The checklist reports scope alignment and reproduction details, while acknowledging missing statistical-significance reporting and documenting limitations and ethical considerations.

NeurIPS Paper Checklist

  • The authors report discussing limitations tied to base-model capacity and future directions in Appendix A.Source: NeurIPS Paper Checklist · p. 20
  • The checklist says core settings include 10 stories of 30 shots each, baselines, models, hardware, and metrics, with code and evaluation scripts intended for release.The pipeline is training-free, so training hyperparameters do not apply.Source: NeurIPS Paper Checklist · pp. 21–22
  • The authors report that quantitative comparisons, human evaluation, and ablations lack error bars, confidence intervals, and statistical tests.Source: NeurIPS Paper Checklist · p. 22
  • The ethics statement addresses energy footprint, misuse risks, and cultural bias, and outlines mitigation measures.Source: NeurIPS Paper Checklist · p. 24
  • The checklist reports that the human study protected participant privacy and followed compensation norms, but provides no formal IRB approval details.The authors describe the study as minimal risk and say participants were informed about the evaluation task.Source: NeurIPS Paper Checklist · p. 26
  • The authors report that LLM usage is a core method component described in the paper’s dynamic storyline modeling and experimental-setting sections.Source: NeurIPS Paper Checklist · p. 26
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