mlsanityData to 14 Oct 2026

3D-Aware Video Generation

arXiv 2206.14797 ↗First posted 29 Jun 2022cs.CV cs.LGcited by 17

Not on the map. Why?

Abstract (arXiv)

Generative models have emerged as an essential building block for many image synthesis and editing tasks. Recent advances in this field have also enabled high-quality 3D or video content to be generated that exhibits either multi-view or temporal consistency. With our work, we explore 4D generative adversarial networks (GANs) that learn unconditional generation of 3D-aware videos. By combining neural implicit representations with time-aware discriminator, we develop a GAN framework that synthesizes 3D video supervised only with monocular videos. We show that our method learns a rich embedding of decomposable 3D structures and motions that enables new visual effects of spatio-temporal renderings while producing imagery with quality comparable to that of existing 3D or video GANs.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 123 of its 157 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

123 of 157 references.

References · 123

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2022Generative Adversarial Networkscited by 5,627
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  7. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  9. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  10. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  11. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  12. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
38 more
  1. 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,234
  2. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  3. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,205
  4. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
  5. 2021Alias-Free Generative Adversarial Networkscited by 1,184
  6. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  7. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
  8. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  9. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
  10. 2020Neural Sparse Voxel Fieldscited by 959
  11. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  12. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  13. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  14. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  15. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  16. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 785
  17. 2020Convolutional Occupancy Networkscited by 761
  18. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 751
  19. 2020Implicit Geometric Regularization for Learning Shapescited by 710
  20. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  21. 2020First Order Motion Model for Image Animationcited by 704
  22. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  23. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 695
  24. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  25. 2018Which Training Methods for GANs do actually Converge?cited by 679
  26. 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
  27. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  28. 2020Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humanscited by 634
  29. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 524
  30. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 496
  31. 2021StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesiscited by 477
  32. 2020Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Videocited by 471
  33. 2020Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstructioncited by 467
  34. 2020One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencingcited by 444
  35. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  36. 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
  37. 2020Local Implicit Grid Representations for 3D Scenescited by 373
  38. 2021Advances in Neural Renderingcited by 365

50 most cited of 123 All 123 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 7; 2024: 5; 2025: 3.

17 papers.

Sort:

By area: