mlsanityData to 14 Oct 2026

One Style is All you Need to Generate a Video

arXiv 2310.17835 ↗First posted 27 Oct 2023cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a style-based conditional video generative model. We introduce a novel temporal generator based on a set of learned sinusoidal bases. Our method learns dynamic representations of various actions that are independent of image content and can be transferred between different actors. Beyond the significant enhancement of video quality compared to prevalent methods, we demonstrate that the disentangled dynamic and content permit their independent manipulation, as well as temporal GAN-inversion to retrieve and transfer a video motion from one content or identity to another without further preprocessing such as landmark points.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 38 of its 48 references that are in the map. First posted on arXiv 27 Oct 2023.

Builds on

38 of 48 references.

References · 38

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  3. 2022Video Diffusion Modelscited by 2,305
  4. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
  5. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
  6. 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,234
  7. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  8. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  9. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 524
  10. 2021Pivotal Tuning for Latent-based Editing of Real Imagescited by 427
  11. 2018Video-to-Video Synthesiscited by 390
  12. 2016Generating Videos with Scene Dynamicscited by 376
26 more
  1. 2017Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolationcited by 326
  2. 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 301
  3. 2015Deep multi-scale video prediction beyond mean square errorcited by 300
  4. 2016Unsupervised Learning for Physical Interaction through Video Predictioncited by 260
  5. 2021Revisiting Skeleton-based Action Recognitioncited by 257
  6. 2018cGANs with Projection Discriminatorcited by 254
  7. 2019Time2Vec: Learning a Vector Representation of Timecited by 213
  8. 2022Thin-Plate Spline Motion Model for Image Animationcited by 210
  9. 2016Temporal Generative Adversarial Nets with Singular Value Clippingcited by 198
  10. 2018X2Face: A network for controlling face generation by using images, audio, and pose codescited by 189
  11. 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
  12. 2023Video Probabilistic Diffusion Models in Projected Latent Spacecited by 171
  13. 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 163
  14. 2017Video Frame Interpolation via Adaptive Convolutioncited by 158
  15. 2017Unsupervised Learning of Disentangled Representations from Videocited by 139
  16. 2018Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GANcited by 120
  17. 2019DwNet: Dense warp-based network for pose-guided human video generationcited by 115
  18. 2022Generating Long Videos of Dynamic Scenescited by 113
  19. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  20. 2020Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolutioncited by 91
  21. 2016An Uncertain Future: Forecasting from Static Images using Variational Autoencoderscited by 87
  22. 2021StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGANcited by 47
  23. 2021From Continuity to Editability: Inverting GANs with Consecutive Imagescited by 25
  24. 2022Collapse by Conditioning: Training Class-conditional GANs with Limited Datacited by 25
  25. 2016Disentangling Space and Time in Video with Hierarchical Variational Auto-encoderscited by 4
  26. 2019Face Video Generation from a Single Image and Landmarkscited by 3

Built on by

Not cited yet.