Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Open accessAbstract
The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many new applications in content creation. In this article, we provide an overview of GANs with a special focus on algorithms and applications for visual synthesis. We cover several important techniques to stabilize GAN training, which has a reputation for being notoriously difficult. We also discuss its applications to image translation, image processing, video synthesis, and neural rendering.
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It is becoming increasingly simple to use pretrained GAN to instantly replace one person's face in an image or video with the face of another person.
“It is becoming increasingly simple to use pretrained GAN to instantly replace one person's face in an image or video with the face of another person (Liu etal. 2021b).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Liu et al. 2021b
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Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyPreprint
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One-Sided Unsupervised Domain MappingPreprint
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Few-shot Video-to-Video SynthesisPreprint
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Implicit Generation and Generalization in Energy-Based ModelsPreprint
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GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
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Deep Learning for Deepfakes Creation and Detection: A Survey
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“nt common underlying mechanism for deepfake creation is deep learning models such as autoencoders and generative adversarial networks, which have been applied widely in the computer vision domain [2]–[4]. These models are used to examine facial expressions and movements of a person and synthesize facial images of anot…”
BackgroundOne-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
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Self-citationPaint by Example: Exemplar-based Image Editing with Diffusion Models
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Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
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Pros and cons of GAN evaluation measures: New developments
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DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
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Generative Adversarial Networks for face generation: A survey
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Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
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RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion
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Self-citationDeepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“Recent advancements in AI-powered deepfakes have even amplified the issue (Liu et al. 2021b).”
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Hong, F.-T., Zhang, L., Shen, L., & Xu, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00339 · Cited by 190 · see citing works
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art
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Next-generation deep learning based on simulators and synthetic data
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Regularizing Generative Adversarial Networks under Limited Data
Tseng, H.-Y., Jiang, L., Liu, C., Yang, M., & Yang, W. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00783 · Cited by 151 · see citing works
Generative Adversarial Networks for Face Generation: A Survey
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Generative Adversarial Networks for Spatio-temporal Data: A Survey
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Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, M. C., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. (2020) · Figshare · doi:10.6084/m9.figshare.12731039.v1 · Cited by 122 · see citing works
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
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Understanding GANs: fundamentals, variants, training challenges, applications, and open problems
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GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
Hao, Z., Mallya, A., Belongie, S., & Liu, M.-Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01381 · Cited by 89 · see citing works
Self-citationAnycost GANs for Interactive Image Synthesis and Editing
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The Role of Generative Adversarial Network in Medical Image Analysis: An In-depth Survey
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Multimodal Conditional Image Synthesis with Product-of-Experts GANs
Huang, X., Mallya, A., Wang, T.-C., & Liu, M.-Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19787-1_6 · Cited by 73 · see citing works
Self-citationA review on Single Image Super Resolution techniques using generative adversarial network
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Application of machine vision in food computing: A review
Zhao, Z., Wang, R., Liu, M., Bai, L., & Sun, Y. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.141238 · Cited by 58 · see citing works
Self-citationCAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
Niemeyer, M., & Geiger, A. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00103 · Cited by 48 · see citing works
VidToMe: Video Token Merging for Zero-Shot Video Editing
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Automated residential layout generation and editing using natural language and images
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The state of art and review on video streaming
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Generative AI in depth: A survey of recent advances, model variants, and real-world applications
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Deep Person Generation: A Survey from the Perspective of Face, Pose, and Cloth Synthesis
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On the Generalization of Deep Learning Models in Video Deepfake Detection
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“For example, reference [5] presents a method for synthesizing realistic talking heads from a single source video.”
MethodA comprehensive survey on generative adversarial networks used for synthesizing multimedia content
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RetractedIntelligent Cinematography: a review of AI research for cinematographic production
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3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
Tang, J., Zhang, B., Yang, B., Zhang, T., Chen, D., Ma, L., & Wen, F. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3323578 · Cited by 29 · see citing works
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Image Synthesis Under Limited Data: A Survey and Taxonomy
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Improving GANs for Long-Tailed Data Through Group Spectral Regularization
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A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
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A bearing surface defect detection method based on multi-attention mechanism Yolov8
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Self-citationA Scoping Review of Literature on Deep Learning Techniques for Face Recognition
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Efficient conditional GAN transfer with knowledge propagation across classes
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One-Shot Free-View Neural Talking-Head Synthesis for Video ConferencingPreprint
Wang, T.-C., Mallya, A., & Liu, M.-Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.15126 · Cited by 22 · see citing works
Self-citationAdvancing Naturalistic Affective Science with Deep Learning
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Exploiting Spatial-temporal Correlations for Video Anomaly Detection
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Perceptual Quality Improvement in Videoconferencing Using Keyframes-Based GAN
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Smart "Error"! Exploring Imperfect AI to Support Creative Ideation
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An Improved Face Image Restoration Method Based on Denoising Diffusion Probabilistic Models
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Hybrid Recurrent Deep Learning Model for DeepFake Video Detection
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DFSGAN: Introducing editable and representative attributes for few-shot image generation
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Conditional Generative Adversarial Networks for modelling fuel sprays
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Subject-independent EEG classification based on a hybrid neural network
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Wav2Lip‐HR: Synthesising clear high‐resolution talking head in the wild
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Image Generation and Recognition Technology Based on Attention Residual GAN
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Bi-EvoGAN: Bi-level Evolutionary Approach for Generative Adversarial Networks
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Appearance and Pose-guided Human Generation: A Survey
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Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement
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Overview of Generative AI Techniques for Cybersecurity
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Human Image Generation: A Comprehensive Survey
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Regularizing Generative Adversarial Networks under Limited DataPreprint
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A Comparative Study on Deepfake Detection Algorithms
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Integration effect of artificial intelligence and traditional animation creation technology
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Impact of face swapping and data augmentation on sign language recognition
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Deepfakes Catcher: A Novel Fused Truncated DenseNet Model for Deepfakes Detection
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Rethinking one-shot face reenactment: A spatial–temporal reconstruction view
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RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
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Video Coding using Learned Latent GAN Compression
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Towards a Throughput and Energy Efficient Association Strategy for Wi-Fi/LiFi Heterogeneous Networks
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Stay Positive: Non-Negative Image Synthesis for Augmented Reality
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Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds
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Advancement in Image Restoration Through GAN-based Approach
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Digital Synthetic Generation From Learning and BigData
Upadhyaya, A. N., Seenivasan, S., Selvakumar, P., Anute, N., Aradhya, S. G. B., Mohit, M., & Manjunath, T. C. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2277-3.ch003 · Cited by 6 · see citing works
Chen, Y., Zeng, R., & Xiong, S. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3323684 · Cited by 7 · see citing works
Gregory, J. M., & Gupta, S. K. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v2i1.27667 · Cited by 7 · see citing works
Towards Scenario-Based Safety Validation for Autonomous Trains with Deep Generative Models
Decker, T. W., Bhattarai, A. R., & Lebacher, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40923-3_20 · Cited by 6 · see citing works
Dewi, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-46238-2_5 · Cited by 6 · see citing works
- Authors
- Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A.
- Affiliations
- Nvidia (United States) (US)
- Google (United States) (US)
- Venue
- Proceedings of the IEEE
- Published
- 2021-05
- Volume
- 109
- Issue
- 5
- Pages
- 839-862
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W3126434301
- Cited by
- 236
- References
- 287
- Citations by year
- 2026: 16 · 2025: 51 · 2024: 63 · 2023: 39 · 2022: 37 · 2021: 27 · 2020: 3
- FWCI
- 14.93
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings of the IEEE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- generative adversarial networks, image generation, video generation, GAN training stability, image-to-image translation, image processing, neural rendering, realistic image synthesis, conditional generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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