Face Transfer

4 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28

Face Transfer is a method for mapping face performances of one individual to facial animations of another one. It uses facial expressions and head poses from the video of a source actor to generate a video of a target character. Face Transfer is a special case of image-to-image translation tasks.

Source: Face Transfer with Generative Adversarial Network

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

VGGFace2 HQ

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Style Transfer

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (8 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models

3 Oct 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified

FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies.
One Shot Face Swapping on Megapixels

11 May 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)

Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain.
MobileFace: 3D Face Reconstruction with Efficient CNN Regression

24 Sep 2018 1 repository listed

Estimation of facial shapes plays a central role for face transfer and animation.
Inverting face embeddings with convolutional neural networks

14 Jun 2016 1 repository listed

Deep neural networks have dramatically advanced the state of the art for many areas of machine learning.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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