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CycleGAN Face-off

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arxiv 1712.03451 v5 pith:SSU3ZRQW submitted 2017-12-09 cs.CV

Xiaohan Jin , Ye Qi , Shangxuan Wu This is my paper · ORCID
classification cs.CV
keywords attributescycleganexpressionsface-offfacialpersontrainingadversarial
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Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to capture details in facial expressions and head poses and thus generate transformation videos of higher consistency and stability.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. One-shot Face Reenactment

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A one-shot face reenactment framework that disentangles appearance and shape, then composes them with a SPADE decoder and a warping-fusion network to transfer pose and expression from a single reference photo.

  2. Video synthesis of human upper body with realistic face

    cs.CV 2019-08 reject novelty 5.0 of 10

    A GAN pipeline transfers a source person's upper-body motion and facial expressions to a target person using body keypoints and facial action units as intermediate representations.

  3. Stitching and dimensionality effects on large artificially generated volume datasets

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    Empirical study shows stitching artifacts in patched cycleGAN volumes evade FID detection yet degrade segmentation performance, with 3D models offering limited benefit over more stable 2D training.

  4. A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models

    cs.CV 2019-08 reject novelty 4.0 of 10

    A virtual anchor face video is synthesized from text by predicting action units and head poses with a Seq2Seq model and rendering frames with a Pix2PixHD generator, as shown in qualitative examples.

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