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Face Transfer with Generative Adversarial Network

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arxiv 1710.06090 v1 pith:YWA4VO2F submitted 2017-10-17 cs.CV

Runze Xu , Zhiming Zhou , Weinan Zhang , Yong Yu This is my paper · ORCID
classification cs.CV
keywords facetransferadversarialcharacterfacialgeneratedgenerativenetwork
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Face transfer animates the facial performances of the character in the target video by a source actor. Traditional methods are typically based on face modeling. We propose an end-to-end face transfer method based on Generative Adversarial Network. Specifically, we leverage CycleGAN to generate the face image of the target character with the corresponding head pose and facial expression of the source. In order to improve the quality of generated videos, we adopt PatchGAN and explore the effect of different receptive field sizes on generated images.

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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. 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.

  4. Face Deepfakes -- A Comprehensive Review

    cs.CV 2025-02 conditional novelty 3.0 of 10

    A review of face deepfake generation and detection finds that off-the-shelf deepfake tools such as Wav2Lip and SimSwap achieve high attack success rates against lightweight face recognition models.

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