Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising
On the map: Diffusion models → Video generation
Abstract (arXiv)
Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.
Key facts. Cited by 139 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 68 of its 76 references that are in the map. First posted on arXiv 29 May 2023.
Builds on
68 of 76 references.
Closest ancestors · 15
Same direction, newest first
- 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators23 Mar
- 2023Pix2Video: Video Editing using Image Diffusion22 Mar
- 2023NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation22 Mar
- 2023FateZero: Fusing Attentions for Zero-shot Text-based Video Editing16 Mar
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- 2023Structure and Content-Guided Video Synthesis with Diffusion Models6 Feb
- 2023Dreamix: Video Diffusion Models are General Video Editors2 Feb
- 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation22 Dec
- 2022SinFusion: Training Diffusion Models on a Single Image or Video21 Nov
- 2022MagicVideo: Efficient Video Generation With Latent Diffusion Models20 Nov
- 2022Imagen Video: High Definition Video Generation with Diffusion Models5 Oct
3 more
Other references · 53
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2017Neural Discrete Representation Learningcited by 4,712
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- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
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- 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,177
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
- 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 906
- 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
- 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
- 2021Vector-quantized Image Modeling with Improved VQGANcited by 658
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
- 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 484
- 2023Video-P2P: Video Editing with Cross-attention Controlcited by 314
- 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 314
- 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
- 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 307
- 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
- 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
- 2022FOSTER: Feature Boosting and Compression for Class-Incremental Learningcited by 229
- 2017Video Generation From Textcited by 154
- 2023Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRAcited by 141
- 2018To Create What You Tell: Generating Videos from Captionscited by 84
- 2021PyCIL: A Python Toolbox for Class-Incremental Learningcited by 83
- 2022NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesiscited by 71
- 2022Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidancecited by 67
- 2021Improving Text-to-Image Synthesis Using Contrastive Learningcited by 62
- 2018Imagine This! Scripts to Compositions to Videoscited by 54
50 most cited of 53 All 53 (JSON)
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