VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation
Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma, Yexin Liu, Wenjie Shu, Yatian Pang, Feilong Tang, Qifeng Chen, Harry Yang, Ser-Nam Lim·December 03, 2024
Summary
VideoGen-of-Thought (VGoT) is a collaborative framework for multi-shot video generation, addressing narrative depth, continuity, and logical coherence. It comprises modules for script, keyframe, shot-level video, and smoothing, designed to create coherent, movie-like videos. VGoT ensures consistency across shots through identity-preserving embeddings and a cross-shot smoothing mechanism, enhancing visual and narrative coherence. The text outlines a method for generating multi-shot videos using one-sentence prompts, focusing on diverse characters' journeys, from a classic American woman to a botanist turned spy. It highlights the use of latents Z to manage noise levels across frames, ensuring coherent video generation despite varying shot content.
Introduction
Background
Overview of video generation challenges
Importance of narrative depth, continuity, and logical coherence in video content
Objective
Aim of VGoT in addressing the aforementioned challenges
Focus on creating coherent, movie-like videos through a collaborative framework
Method
Data Collection
Source materials and datasets used for training
Importance of diverse character journeys in enhancing video realism
Data Preprocessing
Techniques for preparing input data for the VGoT framework
Role of one-sentence prompts in guiding video generation
Script Module
Functionality and role in the VGoT framework
How it contributes to narrative depth and structure
Keyframe Module
Generation and selection of keyframes for video shots
Importance in maintaining visual coherence across shots
Shot-level Video Module
Creation of individual video shots based on keyframes
Techniques for ensuring shot-level coherence and quality
Smoothing Module
Identity-preserving Embeddings
Mechanism for maintaining character consistency across shots
Importance in enhancing narrative coherence
Cross-shot Smoothing
Method for improving visual and narrative flow between shots
Techniques for managing transitions and maintaining continuity
Latents Z Management
Role in controlling noise levels across frames
How latents Z contribute to coherent video generation despite varying shot content
Conclusion
Summary of VGoT's contributions to multi-shot video generation
Future directions and potential improvements
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What role do identity-preserving embeddings and a cross-shot smoothing mechanism play in VGoT?
How does VGoT utilize one-sentence prompts to generate diverse character journeys in multi-shot videos?
What is the main idea of the VideoGen-of-Thought (VGoT) framework?
How does VGoT ensure consistency and coherence across different shots in generated videos?