mlsanityData to 7 Oct 2026

MoCoGAN: Decomposing Motion and Content for Video Generation

arXiv 1707.04993 ↗First posted 17 Jul 2017cs.CVcited by 523

Not on the map. Why?

Cited by 121 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. While the content part is kept fixed, the motion part is realized as a stochastic process. To learn motion and content decomposition in an unsupervised manner, we introduce a novel adversarial learning scheme utilizing both image and video discriminators. Extensive experimental results on several challenging datasets with qualitative and quantitative comparison to the state-of-the-art approaches, verify effectiveness of the proposed framework. In addition, we show that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion.

Key facts. Cited by 523 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 99; 2022: 106; 2023: 106; 2024: 97; 2025: 81; 2026: 34.

523 papers.

Sort:

By area:

  1. 2022Video Diffusion Modelscited by 2,280
  2. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
  3. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  4. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
  5. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
  6. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
  7. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 618
  8. 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
  9. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  10. 2025Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusioncited by 569
  11. 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 568
  12. 2024VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Modelscited by 568
38 more
  1. 2024Latte: Latent Diffusion Transformer for Video Generationcited by 536
  2. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  3. 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 475
  4. 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 463
  5. 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 461
  6. 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Modelscited by 401
  7. 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 385
  8. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 357
  9. 2023LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Modelscited by 349
  10. 2022MAGVIT: Masked Generative Video Transformercited by 341
  11. 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generationcited by 330
  12. 2023MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Modelcited by 330
  13. 2023Pix2Video: Video Editing using Image Diffusioncited by 318
  14. 2023Video-P2P: Video Editing with Cross-attention Controlcited by 312
  15. 2023Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Drivingcited by 309
  16. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 307
  17. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
  18. 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 300
  19. 2023Preserve Your Own Correlation: A Noise Prior for Video Diffusion Modelscited by 284
  20. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 282
  21. 2024Navigation World Modelscited by 262
  22. 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 258
  23. 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 254
  24. 2021Motion Representations for Articulated Animationcited by 253
  25. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  26. 2022CelebV-HQ: A Large-Scale Video Facial Attributes Datasetcited by 246
  27. 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
  28. 2022Diffusion Probabilistic Modeling for Video Generationcited by 237
  29. 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Predictioncited by 231
  30. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  31. 2023A Survey on Video Diffusion Modelscited by 203
  32. 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
  33. 2023DreamVideo: Composing Your Dream Videos with Customized Subject and Motioncited by 186
  34. 2024DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generationcited by 186
  35. 2024VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Timecited by 186
  36. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 181
  37. 2022SimVP: Simpler yet Better Video Predictioncited by 181
  38. 2022Latent Image Animator: Learning to Animate Images via Latent Space Navigationcited by 172

50 most cited of 523 All 523 (JSON)