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VideoPoet: A Large Language Model for Zero-Shot Video Generation

TL;DR

VideoPoet uses a decoder-only Transformer for zero-shot video generation, producing high-quality motions.

cs.CV 🔴 Advanced 2023-12-22 36 views
Dan Kondratyuk Lijun Yu Xiuye Gu José Lezama Jonathan Huang Grant Schindler Rachel Hornung Vighnesh Birodkar Jimmy Yan Ming-Chang Chiu Krishna Somandepalli Hassan Akbari Yair Alon Yong Cheng Josh Dillon Agrim Gupta Meera Hahn Anja Hauth David Hendon Alonso Martinez David Minnen Mikhail Sirotenko Kihyuk Sohn Xuan Yang Hartwig Adam Ming-Hsuan Yang Irfan Essa Huisheng Wang David A. Ross Bryan Seybold Lu Jiang
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video generation large language model zero-shot multimodal Transformer

Key Findings

Methodology

VideoPoet employs a decoder-only Transformer architecture processing multimodal inputs including images, videos, text, and audio. Training involves pretraining with multimodal generative objectives and task-specific adaptation through fine-tuning to enhance generation quality.

Key Results

  • VideoPoet achieved superior CLIP similarity on the MSR-VTT dataset, showcasing its ability to generate high-fidelity motions.
  • On the UCF-101 dataset, VideoPoet's FVD score outperformed other models, proving its superior video generation quality.
  • Experiments demonstrated VideoPoet's capability to generate videos up to 10 seconds in zero-shot conditions, maintaining motion consistency and realism.

Significance

This research breaks the limitations of traditional diffusion models by using large language models for video generation. VideoPoet not only achieves breakthroughs in generation quality but also simplifies multitask processing, offering a more flexible architecture for various video generation tasks.

Technical Contribution

VideoPoet introduces the architecture of large language models into video generation, providing a mechanism distinct from diffusion models, supporting multimodal inputs, enhancing generation quality and task adaptability.

Novelty

VideoPoet is the first to apply large language models to video generation, utilizing multimodal inputs and autoregressive generation mechanisms to achieve different generation quality and task flexibility compared to diffusion models.

Limitations

  • In extremely complex scenarios, generation quality may degrade, requiring further optimization of the model architecture.
  • The model requires more fine-tuning on certain tasks to achieve optimal performance.

Future Work

Future research could explore enhancing the model's generation capabilities in complex scenarios, expanding the range of multimodal inputs, and optimizing fine-tuning strategies during task adaptation.

AI Executive Summary

VideoPoet is a large language model for zero-shot video generation using a decoder-only Transformer architecture to process multimodal inputs. Existing video generation models often use diffusion models, which perform well for single-frame images but face consistency issues in generating continuous video. VideoPoet addresses this through pretraining and task adaptation phases, combining multimodal generative objectives.

The model demonstrates excellent performance across multiple datasets, particularly in generating high-fidelity motions. Experiments show VideoPoet can generate videos up to 10 seconds in zero-shot conditions, maintaining motion consistency and realism. Its flexible architecture allows for handling multiple tasks within the same model, simplifying the complexity of multitask processing.

While VideoPoet achieves breakthroughs in generation quality, challenges remain in handling extremely complex scenarios. Future research could explore enhancing the model's generation capabilities in complex scenarios, expanding the range of multimodal inputs, and optimizing fine-tuning strategies during task adaptation.

Deep Analysis

Background

Video generation technology has seen significant advancements recently, particularly diffusion models performing well in single-frame image generation. However, they often face consistency issues in generating continuous video. Large language models show potential in multimodal processing, but their application in video generation remains limited.

Core Problem

Existing video generation models often face consistency issues when generating continuous video, especially in complex scenarios. The core problem is leveraging large language models' multimodal processing capabilities to address this issue.

Innovation

VideoPoet uses a decoder-only Transformer architecture to process multimodal inputs, achieving a generation mechanism distinct from diffusion models. Its pretraining phase combines multimodal generative objectives, and the task adaptation phase enhances generation quality through fine-tuning.

Methodology

  • �� Use decoder-only Transformer architecture to process multimodal inputs
  • �� Pretraining phase combines multimodal generative objectives
  • �� Task adaptation phase enhances generation quality through fine-tuning
  • �� Supports various video generation tasks, including text-to-video, image-to-video, etc.

Experiments

Experiments were conducted on multiple datasets, including MSR-VTT, UCF-101, etc. Evaluation metrics included CLIP similarity, FVD scores, etc. The experimental design considered various task scenarios, validating the model's zero-shot generation capabilities.

Results

VideoPoet achieved superior CLIP similarity on the MSR-VTT dataset and FVD scores on the UCF-101 dataset, proving its superior video generation quality.

Applications

VideoPoet can be used for various video generation tasks, such as text-to-video, image-to-video, etc. Its flexible architecture allows handling multiple tasks within the same model, simplifying the complexity of multitask processing.

Limitations & Outlook

In extremely complex scenarios, generation quality may degrade, requiring further optimization of the model architecture. The model requires more fine-tuning on certain tasks to achieve optimal performance.

Plain Language Accessible to non-experts

Imagine you're in a kitchen, and VideoPoet is like a versatile chef who can create delicious dishes (videos) from various ingredients (images, videos, text, and audio). It learns various recipes (pretraining) and tries new dishes (task adaptation), eventually making tasty dishes without specific instructions.

ELI14 Explained like you're 14

Imagine you're playing a game, and VideoPoet is like a super game character who can perform different actions based on different tasks (like fighting monsters, solving puzzles). It practices and learns new skills, eventually completing various tasks without specific instructions.

Glossary

Transformer

A neural network architecture for processing sequence data, widely used in natural language processing.

Used in VideoPoet to process multimodal inputs.

Zero-shot

The ability to perform tasks without specific training data.

VideoPoet can generate videos in zero-shot conditions.

Multimodal

Involves processing multiple data types, such as images, text, audio, etc.

VideoPoet processes multimodal inputs to generate videos.

CLIP similarity

A metric for measuring the similarity between generated videos and text descriptions.

Used to evaluate VideoPoet's performance in text-to-video tasks.

FVD

A metric for evaluating the quality of generated videos, with lower values indicating higher quality.

Used to evaluate VideoPoet's performance in video generation tasks.

Open Questions Unanswered questions from this research

  • 1 How to further optimize VideoPoet's generation quality in extremely complex scenarios remains to be explored.
  • 2 Expanding the range of multimodal inputs to enhance model flexibility is a future research direction.

Applications

Immediate Applications

Video Editing

VideoPoet can be used for rapid video content generation and editing, suitable for film production and advertising industries.

Educational Video Generation

Generate educational videos from text, helping teachers quickly create teaching content.

Long-term Vision

Virtual Reality Content Creation

VideoPoet can be used to generate high-quality virtual reality content, driving development in entertainment and education fields.

Abstract

We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that of Large Language Models (LLMs), consisting of two stages: pretraining and task-specific adaptation. During pretraining, VideoPoet incorporates a mixture of multimodal generative objectives within an autoregressive Transformer framework. The pretrained LLM serves as a foundation that can be adapted for a range of video generation tasks. We present empirical results demonstrating the model's state-of-the-art capabilities in zero-shot video generation, specifically highlighting VideoPoet's ability to generate high-fidelity motions. Project page: http://sites.research.google/videopoet/

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