mlsanityData to 7 Oct 2026

InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions

arXiv 2402.03040 ↗First posted 5 Feb 2024cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

We introduce textitInteractiveVideo, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction, allowing users to instruct the generative model through various intuitive mechanisms during the whole generation process, e.g. text and image prompts, painting, drag-and-drop, etc. We propose a Synergistic Multimodal Instruction mechanism, designed to seamlessly integrate users' multimodal instructions into generative models, thus facilitating a cooperative and responsive interaction between user inputs and the generative process. This approach enables iterative and fine-grained refinement of the generation result through precise and effective user instructions. With textitInteractiveVideo, users are given the flexibility to meticulously tailor key aspects of a video. They can paint the reference image, edit semantics, and adjust video motions until their requirements are fully met. Code, models, and demo are available at https://github.com/invictus717/InteractiveVideo

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 44 of its 47 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

44 of 47 references.

References · 44

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  5. 2020Denoising Diffusion Implicit Modelscited by 9,655
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2017Deep reinforcement learning from human preferencescited by 3,846
  8. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  9. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  10. 2022Video Diffusion Modelscited by 2,280
  11. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  12. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
32 more
  1. 2018Group Normalizationcited by 1,514
  2. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  3. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
  4. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  5. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
  6. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 666
  7. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
  8. 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
  9. 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 475
  10. 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 463
  11. 2023DynamiCrafter: Animating Open-domain Images with Video Diffusion Priorscited by 463
  12. 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 461
  13. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  14. 2023Aligning Text-to-Image Models using Human Feedbackcited by 418
  15. 2023I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Modelscited by 343
  16. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
  17. 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 300
  18. 2023Human Preference Score: Better Aligning Text-to-Image Models with Human Preferencecited by 284
  19. 2023DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectorycited by 260
  20. 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 254
  21. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  22. 2022MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generationcited by 225
  23. 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
  24. 2023Video Probabilistic Diffusion Models in Projected Latent Spacecited by 171
  25. 2017Video Generation From Textcited by 153
  26. 2023Meta-Transformer: A Unified Framework for Multimodal Learningcited by 136
  27. 2022gDDIM: Generalized denoising diffusion implicit modelscited by 125
  28. 2018To Create What You Tell: Generating Videos from Captionscited by 84
  29. 2023UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognitioncited by 64
  30. 2016Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architecturescited by 44
  31. 2022Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generationcited by 32
  32. 2023PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Modelscited by 30

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.