Latent Video Diffusion Models for High-Fidelity Long Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
AI-generated content has attracted lots of attention recently, but photo-realistic video synthesis is still challenging. Although many attempts using GANs and autoregressive models have been made in this area, the visual quality and length of generated videos are far from satisfactory. Diffusion models have shown remarkable results recently but require significant computational resources. To address this, we introduce lightweight video diffusion models by leveraging a low-dimensional 3D latent space, significantly outperforming previous pixel-space video diffusion models under a limited computational budget. In addition, we propose hierarchical diffusion in the latent space such that longer videos with more than one thousand frames can be produced. To further overcome the performance degradation issue for long video generation, we propose conditional latent perturbation and unconditional guidance that effectively mitigate the accumulated errors during the extension of video length. Extensive experiments on small domain datasets of different categories suggest that our framework generates more realistic and longer videos than previous strong baselines. We additionally provide an extension to large-scale text-to-video generation to demonstrate the superiority of our work. Our code and models will be made publicly available.
Key facts. Cited by 385 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 45 of its 47 references that are in the map. First posted on arXiv 23 Nov 2022.
Builds on
45 of 47 references.
Closest ancestors · 4
Same direction, newest first
- 2022MagicVideo: Efficient Video Generation With Latent Diffusion Models20 Nov
- 2022Imagen Video: High Definition Video Generation with Diffusion Models5 Oct
- 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation19 May
- 2022Video Diffusion Models7 Apr
Other references · 41
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2022Classifier-Free Diffusion Guidancecited by 5,717
29 more
- 2017Neural Discrete Representation Learningcited by 4,650
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
- 2021Alias-Free Generative Adversarial Networkscited by 1,181
- 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
- 2020First Order Motion Model for Image Animationcited by 698
- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
- 2016Generating Videos with Scene Dynamicscited by 376
- 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
- 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
- 2018Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GANcited by 120
- 2022Generating Long Videos of Dynamic Scenescited by 113
- 2020Latent Video Transformercited by 106
- 2017Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networkscited by 88
- 2019VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generationcited by 74
- 2021Predicting Video with VQVAEcited by 61
- 2018Probabilistic Video Generation using Holistic Attribute Controlcited by 24
- 2021Hierarchical Video Generation for Complex Datacited by 3
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Citing papers per year: 2023: 26; 2024: 119; 2025: 177; 2026: 63.
385 papers.
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