FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment
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Abstract (arXiv)
We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces. We derive a novel iterative deep learning--based approach for face reenactment which adjusts significant pose and expression variations that can be applied to a single image or a video sequence. For video sequences, we introduce a continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving the target skin color and lighting conditions. This network uses a novel Poisson blending loss combining Poisson optimization with a perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior. This work describes extensions of the FSGAN method, proposed in an earlier conference version of our work, as well as additional experiments and results.
Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 51 of its 83 references that are in the map. First posted on arXiv 25 Feb 2022.
Builds on
51 of 83 references.
References · 51
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- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
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- 2017How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)cited by 524
- 2019FSGAN: Subject Agnostic Face Swapping and Reenactmentcited by 319
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- 2018Deep Video Portraitscited by 265
- 2019Neural Voice Puppetry: Audio-driven Facial Reenactmentcited by 249
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50 most cited of 51 All 51 (JSON)
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Citing papers per year: 2022: 3; 2023: 8; 2024: 12; 2025: 8; 2026: 2.
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