mlsanityData to 7 Oct 2026

ReenactGAN: Learning to Reenact Faces via Boundary Transfer

arXiv 1807.11079 ↗First posted 29 Jul 2018cs.CV cs.AI cs.GRcited by 85

Not on the map. Why?

Abstract (arXiv)

We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from monocular video input of an arbitrary person to a target person. Instead of performing a direct transfer in the pixel space, which could result in structural artifacts, we first map the source face onto a boundary latent space. A transformer is subsequently used to adapt the boundary of source face to the boundary of target face. Finally, a target-specific decoder is used to generate the reenacted target face. Thanks to the effective and reliable boundary-based transfer, our method can perform photo-realistic face reenactment. In addition, ReenactGAN is appealing in that the whole reenactment process is purely feed-forward, and thus the reenactment process can run in real-time (30 FPS on one GTX 1080 GPU). Dataset and model will be publicly available at https://wywu.github.io/projects/ReenactGAN/ReenactGAN.html

Key facts. Cited by 85 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jul 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 27; 2023: 15; 2024: 10; 2025: 7; 2026: 1.

85 papers.

Sort:

By area:

  1. 2021Multi-attentional Deepfake Detectioncited by 339
  2. 2021Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representationcited by 288
  3. 2022CelebV-HQ: A Large-Scale Video Facial Attributes Datasetcited by 246
  4. 2021PIRenderer: Controllable Portrait Image Generation via Semantic Neural Renderingcited by 202
  5. 2022Depth-Aware Generative Adversarial Network for Talking Head Video Generationcited by 177
  6. 2022StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGANcited by 169
  7. 2022EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Modelcited by 151
  8. 2021M2TR: Multi-modal Multi-scale Transformers for Deepfake Detectioncited by 114
  9. 2023CelebV-Text: A Large-Scale Facial Text-Video Datasetcited by 103
  10. 2022Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesiscited by 93
  11. 2021Face Forensics in the Wildcited by 87
  12. 2021Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forwardcited by 82
38 more
  1. 2021Responsive Listening Head Generation: A Benchmark Dataset and Baselinecited by 67
  2. 2023Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generationcited by 61
  3. 2022MARLIN: Masked Autoencoder for facial video Representation LearnINgcited by 55
  4. 2021OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wildcited by 50
  5. 2022Deepfake Detection: A Comprehensive Survey from the Reliability Perspectivecited by 48
  6. 2021Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generationcited by 46
  7. 2022Implicit Warping for Animation with Image Setscited by 45
  8. 2023DPE: Disentanglement of Pose and Expression for General Video Portrait Editingcited by 45
  9. 2023RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatarscited by 45
  10. 2022Voice-Face Homogeneity Tells Deepfakecited by 44
  11. 2023High-Fidelity and Freely Controllable Talking Head Video Generationcited by 42
  12. 2022Controllable 3D Face Synthesis with Conditional Generative Occupancy Fieldscited by 37
  13. 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
  14. 2021Initiative Defense against Facial Manipulationcited by 36
  15. 2022FSGANv2: Improved Subject Agnostic Face Swapping and Reenactmentcited by 33
  16. 2021Learned Spatial Representations for Few-shot Talking-Head Synthesiscited by 32
  17. 2024LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarkscited by 21
  18. 2021Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesiscited by 16
  19. 2023Emotionally Enhanced Talking Face Generationcited by 14
  20. 2024FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Featurescited by 14
  21. 2021Image-to-Video Generation via 3D Facial Dynamicscited by 13
  22. 2021One-shot Face Reenactment Using Appearance Adaptive Normalizationcited by 12
  23. 2022Learning Disentangled Representation for One-shot Progressive Face Swappingcited by 12
  24. 2021UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editingcited by 11
  25. 2023AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agentscited by 11
  26. 2021Everything's Talkin': Pareidolia Face Reenactmentcited by 10
  27. 2022Compressing Video Calls using Synthetic Talking Headscited by 10
  28. 2023Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesiscited by 10
  29. 2021Metric Learning for Anti-Compression Facial Forgery Detectioncited by 9
  30. 2021LI-Net: Large-Pose Identity-Preserving Face Reenactment Networkcited by 9
  31. 2023Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generatorcited by 9
  32. 2023From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applicationscited by 9
  33. 2023POCE: Pose-Controllable Expression Editingcited by 8
  34. 2023Text-image guided Diffusion Model for generating Deepfake celebrity interactionscited by 8
  35. 2024Diffusion Deepfakecited by 8
  36. 2024Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluationscited by 8
  37. 2024Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generationcited by 8
  38. 2021Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectivescited by 6

50 most cited of 85 All 85 (JSON)