文本到视频生成：研究现状、进展和挑战

邓梓焌; 何相腾; 彭宇新

doi:10.11999/JEIT240074

文本到视频生成：研究现状、进展和挑战

doi: 10.11999/JEIT240074

北京大学王选计算机研究所北京 100080

基金项目: 国家自然科学基金(61925201, 62132001, 62272013)

详细信息

作者简介:
邓梓焌：男，博士生，研究方向为文本到视频生成

何相腾：男，博士，助理研究员，研究方向为跨媒体分析、细粒度图像分类、图像视频内容理解、计算机视觉和人工智能

彭宇新：男，博士，教授，博士生导师，研究方向为跨媒体分析与推理、图像视频识别与理解、计算机视觉和人工智能

通讯作者:
彭宇新　pengyuxin@pku.edu.cn

中图分类号: TN911.6; TP18
计量
- 文章访问数: 168
- HTML全文浏览量: 90
- PDF下载量: 24
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-30
- 修回日期: 2024-04-30
- 网络出版日期: 2024-05-12
- 刊出日期: 2024-05-30

Text-to-video Generation: Research Status, Progress and Challenges

Wangxuan Institute of Computer Technology, Peking University, Beijing 100080, China

Funds: The National Natural Science Foundation of China (61925201, 62132001, 62272013)

摘要

摘要: 文本到视频生成旨在根据用户给定的文本描述生成语义一致、内容真实、时序连贯且符合逻辑的视频。该文首先介绍了文本到视频生成领域的研究现状，详细介绍了3类主流的文本到视频生成方法：基于循环网络与生成对抗网络(GAN)的生成方法，基于Transformer的生成方法和基于扩散模型的生成方法。这3类生成方法在视频生成任务上各有优劣：基于循环网络与生成对抗网络的生成方法能生成较高分辨率和时长的视频，但难以生成复杂的开放域视频；基于Transformer的生成方法有能力生成复杂的开放域视频，但受限于Transformer模型单向偏置、累计误差等问题，难以生成高保真视频；扩散模型具有很好的泛化性，但受制于推理速度和高昂的内存消耗，难以生成高清的长视频。然后，该文介绍了文本到视频生成领域的评测基准和指标，并分析比较了现有主流方法的性能。最后，展望了未来可能的研究方向。
- 文本到视频生成 /
- 扩散模型 /
- 生成对抗网络.
Abstract: The generation of video from text aims to produce semantically consistent, photo-realistic, temporal consistent, and logically coherent videos based on provided textual descriptions. Firstly, the current state of research in the field of text-to-video generation is elucidated in this paper, providing a detailed overview of three mainstream approaches: methods based on recurrent networks and Generative Adversarial Networks (GAN), methods based on Transformers, and methods based on diffusion models. Each of these models has its strengths and weaknesses in video generation. The recurrent networks and GAN-based methods can generate videos with higher resolution and duration but struggle with generating complex open-domain videos. Transformer-based methods show proficiency in generating open-domain videos but face challenges related to unidirectional biases and accumulated errors, making it difficult to produce high-fidelity videos. Diffusion models exhibit good generalization but are constrained by inference speed and high memory consumption, making it challenging to generate high-definition and lengthy videos. Subsequently, evaluation benchmarks and metrics in the text-to-video generation domain are explored, and the performance of existing methods is compared. Finally, potential future research directions in the field is outlined.
- Text-to-video generation /
- Diffusion model /
- Generative Adversarial Network (GAN)

HTML全文

图 1 文本到视频生成现有方法概览

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图 2 文本到视频生成任务示意图

下载: 全尺寸图片幻灯片

图 3 一些现有方法的可视化比较^[53]

下载: 全尺寸图片幻灯片

表 1 UCF-101数据集上的比较

方法	发表会议或期刊	FVD ↓
MoCoGAN-HD^[14]	ICLR 2018	700.0
StyleGAN-V^[17]	CVPR 2022	1431.0
MCVD^[13]	NeurIPS 2022	1143.0
DIGAN^[15]	ICLR 2021	577.0
MV-Diffusion^[46]	ACM MM 2023	492.6
CogVideo^[26]	ICLR 2023	305.0
Show-1^[53]	arxiv 2023	394.5
SVD^[52]	arxiv 2023	242.0
VideoFusion^[37]	CVPR 2023	173.0

下载: 导出CSV

表 2 MSR-VTT数据集上的比较

方法	发表会议或期刊	FVD ↓	CLIPSIM ↑
NÜWA^[25]	ECCV 2022	47.68	0.2439
CogVideo (Chinese)^[26]	ICLR 2023	24.78	0.2614
CogVideo (English)^[26]	ICLR 2023	23.59	0.2631
Make-A-Video^[35]	ICLR2023	13.17	0.3049
Show-1^[53]	arxiv 2023	13.08	0.3072
ModelScopeT2V^[38]	arxiv 2023	11.09	0.2930

下载: 导出CSV

表 3 人工评价比较

方法	视觉质量 ↑	文本一致性 ↑	动作质量↑	时序连贯性↑
ModelScopeT2V^[38]	55.23	47.22	59.41	59.31
Zeroscope^[41]	56.37	46.18	54.26	61.19
Pika^[40]	63.52	54.11	57.74	69.35
Gen2^[39]	67.35	52.30	62.53	69.71

下载: 导出CSV

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