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

Abstract: 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 first converts the user prompt into concise shot descriptions, then elaborates them into detailed, cinematic specifications across five domains (character dynamics, background continuity, relationship evolution, camera movements, HDR lighting), ensuring logical narrative progression with self-validation. (2) Visual Inconsistency: Existing approaches struggle with maintaining visual consistency across shots. Our identity-aware cross-shot propagation generates identity-preserving portrait (IPP) tokens that maintain character fidelity while allowing trait variations (expressions, aging) dictated by the storyline. (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. 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, while achieving over 100% better cross-shot consistency and 10x fewer manual adjustments than alternatives.

• The paper introduces a modular framework that decomposes video generation into script, keyframe, shot-level, and smooth modules for improved coherence.
• It employs advanced diffusion models and large language models to transform detailed textual prompts into visually consistent multi-shot sequences.
• User studies and quantitative metrics demonstrate superior facet and style consistency, marking a significant advancement in automated video storytelling.

Overview of VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation

The paper "VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation" addresses the persistent challenge in the domain of video generation involving the creation of cohesive, multi-shot videos from textual prompts. This study recognizes the limitations of existing video generation models, which excel at producing visually appealing short clips but struggle to maintain logical and visual coherence across multiple interconnected shots. The authors propose a novel framework, VideoGen-of-Thought (VGoT), aimed at overcoming these challenges by adopting a structured, modular approach.

VGoT distinguishes itself by decomposing the video generation task into four interdependent modules: Script Generation, Keyframe Generation, Shot-Level Video Generation, and Smooth Module. Such a modular architecture enables the generation of coherent video sequences, where each module contributes a specific aspect to the overall process.

Modular Approach to Video Generation

1. Script Generation: The process begins with converting a high-level user story into detailed shot descriptions using a LLM. This module produces specifications for character, background, relations, camera pose, and lighting, setting the stage for the subsequent keyframe generation.
2. Keyframe Generation: Leveraging text-to-image diffusion models, this module creates consistent keyframes for the generated script. The use of identity-preserving (IP) embeddings ensures continuity in character portrayal across various clips.
3. Shot-Level Video Generation: Employing a video diffusion model, this module synthesizes video latents driven by the generated keyframes and script. The resulting video clips are enriched with content dynamics.
4. Smooth Module: This ensures smooth transitions between shots, maintaining temporal and visual coherence throughout the video sequence by introducing a cross-shot smoothing mechanism.

Experimental Results

The evidence provided by the authors showcases the strength of VGoT compared to contemporary methods such as EasyAnimate, CogVideo, and VideoCrafter. The VGoT framework demonstrates superior Fac Consistency (FC) and Style Consistency (SC) scores, particularly in maintaining these metrics across multiple shots. Quantitative results indicate a measurable improvement in narrative coherence and visual fidelity of the generated videos. Additionally, a user study affirms the framework's effectiveness, with participants preferring VGoT-generated content due to its superior cross-shot consistency and visual quality.

Theoretical and Practical Implications

Theoretically, the paper demonstrates the efficacy of breaking down complex video generation into modular tasks, each optimized to handle specific components of video narrative and cohesion. Practically, VGoT offers a robust tool for creators needing to automate storytelling in video formats, with potential applications spanning entertainment, advertising, and education.

Future Directions

The paper acknowledges current limitations, such as the use of single IP embeddings per shot, which could constrain the portrayal of complex multi-character scenes. Future work could explore more sophisticated mechanisms for handling multiple character interactions and expanding the suite of evaluation metrics to better capture narrative depth and coherency.

In summation, VGoT presents a significant step forward in the quest to generate coherent and contextually rich multi-shot videos from text, merging the boundaries of language and visual generation with a methodical and structured approach. As the field progresses, such innovations will undoubtedly contribute to more nuanced and lifelike video content generation.