A Comprehensive Survey on Long Video Generation: Challenges, Methods, and Prospects

핵심 개념

Long video generation research explores challenges, methods, and prospects for advancing the field.

초록

This comprehensive survey delves into challenges, methods, and prospects in long video generation research. It covers paradigms like Divide and Conquer and AutoRegressive, models like diffusion models and GANs, as well as datasets and evaluation metrics used in the field. The content is segmented into sections discussing basic video generation techniques, long video generation paradigms, improvements in quality characteristics of long videos, resource-saving strategies for computational efficiency, future directions for research advancements.
Basic Video Generation Techniques:

Diffusion models refine iterative refinement process for videos.
Spatial Auto-regressive models synthesize content through patch-based methodology.
GANs turn noise patterns into video frames progressively shaping them.
Control signals for video generation:

Text prompts guide model to generate relevant content based on descriptions.
Image prompts influence visual style within generated videos.
Long Video Generation Paradigms:

Divide And Conquer paradigm identifies keyframes followed by generating intervening frames.
Temporal Autoregressive paradigm generates short video segments based on prior conditions.
Improve Temporal-Spatial Consistency:

Model enhancements add layers to capture temporal-spatial features effectively.
Preceding conditions modeling plays a significant role in bolstering temporal-spatial consistency.
Improve Content Continuity:

Model structural enhancements decompose frames into shared components to ensure continuity.
Improve Diversity of Long Video:

Resolution improvement aims at generating high-resolution long videos with variable sizes.
Computational Resources:

Resource-saving data compression techniques minimize data dimensionality reducing computational complexity.

통계

The distinction between long and short videos often relies on relative measures such as frame count or duration compared to shorter videos.
Yin et al. (2023) successfully generated videos with up to 1024 frames using a divide-and-conquer diffusion structure specifically trained on long videos.
Zhuang et al. (2024) extended input text into scripts to guide the generation of minute-level long videos leveraging Large Language Models (LLM).
Sora (OpenAI, 2024) achieved high-fidelity seamlessly generating long videos up to one minute in duration featuring multi-resolution effects.

인용구

"Videos are classified as 'long' if their duration exceeds 10 seconds or comprises more than 100 frames." - Research Definition
"Efforts are focused on enhancing data processing and structural optimization to address challenges like frame skipping and motion inconsistencies." - Research Focus
"Future studies will likely emphasize improving video generation’s flexibility for real-world applications." - Future Direction

핵심 통찰 요약

A Survey on Long Video Generation

by Chengxuan Li... 게시일 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16407.pdf

더 깊은 질문

How can researchers address the scarcity of long video datasets for training purposes?

To address the scarcity of long video datasets for training, researchers can employ innovative methods such as data augmentation, transfer learning, and dataset synthesis. Data augmentation techniques involve creating variations of existing data by applying transformations like rotation, scaling, and flipping to increase dataset size. Transfer learning allows models pre-trained on large-scale datasets to be fine-tuned on smaller long video datasets, leveraging knowledge from the larger dataset. Additionally, dataset synthesis involves generating new data samples based on existing ones using techniques like generative adversarial networks (GANs) or language models.

What are the potential benefits of developing a unified paradigm integrating strengths from both Divide And Conquer and Temporal Autoregressive approaches?

Developing a unified paradigm that integrates strengths from both Divide And Conquer and Temporal Autoregressive approaches could lead to more robust and versatile long video generation models. By combining the hierarchical structure of Divide And Conquer with the sequential nature of Temporal Autoregressive modeling, researchers can potentially achieve better temporal-spatial consistency in generated videos while maintaining content continuity across frames. This integration may also enhance model flexibility in handling variable lengths and aspect ratios in videos.

How can generative models be enhanced for better controllability in real-world applications beyond black-box operations?

Generative models can be enhanced for better controllability in real-world applications by incorporating mechanisms for explicit conditioning during generation. This includes providing specific control signals such as text prompts or image inputs to guide the generation process towards desired outcomes. Techniques like mask modeling can help focus on relevant parts of input data while ignoring irrelevant information, improving interpretability and control over generated outputs. Additionally, introducing feedback loops or reinforcement learning strategies can enable interactive adjustments during generation to ensure outputs align with user-defined criteria.