2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer
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Abstract (arXiv)
Recent studies have used GAN to transfer expressions between human faces. However, existing models have many flaws: relying on emotion labels, lacking continuous expressions, and failing to capture the expression details. To address these limitations, we propose a novel CycleGAN- and InfoGAN-based network called 2 Cycles Expression Transfer GAN (2CET-GAN), which can learn continuous expression transfer without using emotion labels. The experiment shows our network can generate diverse and high-quality expressions and can generalize to unknown identities. To the best of our knowledge, we are among the first to successfully use an unsupervised approach to disentangle expression representation from identities at the pixel level.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 18 Nov 2022.
Builds on
23 of 37 references.
References · 23
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11 more
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.