CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
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Cited by 391 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-video datasets hinder the model understanding complex movement semantics. In this work, we present 9B-parameter transformer CogVideo, trained by inheriting a pretrained text-to-image model, CogView2. We also propose multi-frame-rate hierarchical training strategy to better align text and video clips. As (probably) the first open-source large-scale pretrained text-to-video model, CogVideo outperforms all publicly available models at a large margin in machine and human evaluations.
Key facts. Cited by 973 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 29 May 2022.
Builds on
35 of 45 references.
References · 35
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- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2022Generative Adversarial Networkscited by 5,603
- 2017Neural Discrete Representation Learningcited by 4,650
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
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23 more
- 2020Extracting Training Data from Large Language Modelscited by 2,144
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
- 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 946
- 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 852
- 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
- 2019VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Researchcited by 412
- 2016Generating Videos with Scene Dynamicscited by 376
- 2018A Short Note about Kinetics-600cited by 331
- 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
- 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 256
- 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 254
- 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
- 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
- 2016Temporal Generative Adversarial Nets with Singular Value Clippingcited by 198
- 2019Scaling Autoregressive Video Modelscited by 189
- 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
- 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
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- 2022Transframer: Arbitrary Frame Prediction with Generative Modelscited by 37
Built on by
Citing papers per year: 2022: 17; 2023: 143; 2024: 222; 2025: 366; 2026: 225.
973 papers.
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