VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning (2309.15091v2)

Abstract: Recent text-to-video (T2V) generation methods have seen significant advancements. However, the majority of these works focus on producing short video clips of a single event (i.e., single-scene videos). Meanwhile, recent LLMs have demonstrated their capability in generating layouts and programs to control downstream visual modules. This prompts an important question: can we leverage the knowledge embedded in these LLMs for temporally consistent long video generation? In this paper, we propose VideoDirectorGPT, a novel framework for consistent multi-scene video generation that uses the knowledge of LLMs for video content planning and grounded video generation. Specifically, given a single text prompt, we first ask our video planner LLM (GPT-4) to expand it into a 'video plan', which includes the scene descriptions, the entities with their respective layouts, the background for each scene, and consistency groupings of the entities. Next, guided by this video plan, our video generator, named Layout2Vid, has explicit control over spatial layouts and can maintain temporal consistency of entities across multiple scenes, while being trained only with image-level annotations. Our experiments demonstrate that our proposed VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with consistency, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation. Detailed ablation studies, including dynamic adjustment of layout control strength with an LLM and video generation with user-provided images, confirm the effectiveness of each component of our framework and its future potential.

Analysis of "VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning"

The paper introduces VideoDirectorGPT, an innovative framework that leverages LLMs for creating temporally consistent multi-scene video generation. This framework represents a methodological departure from existing text-to-video (T2V) generation models that predominantly focus on single-scene video outputs. The authors propose a two-phase approach consisting of video content planning guided by LLMs, specifically GPT-4, followed by video generation or rendering using their proposed Layout2Vid module. The primary goal is to utilize LLMs' capability of understanding and expanding upon textual descriptions to better structure multi-scene videos with enhanced visual consistency and layout control.

Methodological Approach

1. Video Planning by LLMs: The initial phase involves employing GPT-4 to transform a singular textual prompt into a comprehensive multi-scene video plan. This involves generating scene descriptions, identifying and arranging entities with spatial layouts, defining backgrounds, and establishing consistency groupings for components that reappear. The planning process is bifurcated into expanding the scene overview and detailing frame-by-frame entity layouts.
2. Video Generation using Layout2Vid: The second stage deploys the Layout2Vid module to actualize the video plan into coherent video output. Layout2Vid was built upon the existing ModelScopeT2V framework, enriching it with explicit spatial layout control and temporal consistency without requiring video-level training data. This enhancement is achieved through Guided 2D Attention mechanisms, using shared representations for entities and employing joint embeddings derived from image and text inputs.

Empirical Evaluation

The experiments demonstrate that VideoDirectorGPT markedly improves performance across several tasks compared to baselines, particularly the ModelScopeT2V.

• Single-scene Generation: VideoDirectorGPT exhibits superior control over spatial layouts, object counts, spatial relations, scale, and object dynamics. It achieves remarkable accuracy in VPEval Skill-based tasks (object, count, spatial, and scale) and ActionBench-Direction tests, showcasing its proficiency in layout steering and movement depiction.
• Multi-scene Generation: The framework generates videos with higher visual consistency across scenes, as evidenced by evaluations on datasets like ActivityNet Captions, Coref-SV, and HiREST. Consistency metrics reveal significant improvements, suggesting enhanced entity identity preservation through LLM-generated layouts.
• Open-Domain Evaluation: Using the MSR-VTT dataset, VideoDirectorGPT maintains competitive performance in terms of visual quality (FID, FVD) and text-video alignment against industry-leading models, affirming the foundational capabilities of ModelScopeT2V while integrating novel layout controls.

Implications and Future Directions

The integration of LLMs with video generation elucidates potential avenues for creating more sophisticated and coherent narrative structures in AI-generated content. VideoDirectorGPT not only improves on the technical aspects of video layout and consistency but also symbolizes a shift towards more semantically rich content creation, potentially impacting domains like media production, education, and entertainment.

Future explorations could delve into optimizing the computational efficiency of LLM-guided processes and enhancing the extensibility of frameworks like Layout2Vid to support diverse multimedia formats. Additionally, leveraging open-source models or advances in LLM training may lead to more cost-effective implementations, broadening accessibility and scalability.

In summary, VideoDirectorGPT stands as a significant progression in the landscape of multi-scene video generation, harnessing the planning capabilities embedded in LLMs to produce visually and temporally refined video outputs. This work lays the groundwork for subsequent innovations in content creation and digital storytelling.