Face Reenactment
24 papers with code • 0 benchmarks • 1 datasets
Face Reenactment is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.
Source: One-shot Face Reenactment
Benchmarks
These leaderboards are used to track progress in Face Reenactment
AnimeCeleb
Latest papers with no code
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features
The task of face reenactment is to transfer the head motion and facial expressions from a driving video to the appearance of a source image, which may be of a different person (cross-reenactment).
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
To this end, in this paper we present DiffusionAct, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment.
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset
The existing facial datasets, while having plentiful images at near frontal views, lack images with extreme head poses, leading to the downgraded performance of deep learning models when dealing with profile or pitched faces.
Learning Dense Correspondence for NeRF-Based Face Reenactment
Therefore, we are inspired to ask: Can we learn the dense correspondence between different NeRF-based face representations without a 3D parametric model prior?
MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment
We present a novel end-to-end identity-agnostic face reenactment system, MaskRenderer, that can generate realistic, high fidelity frames in real-time.
ToonTalker: Cross-Domain Face Reenactment
Moreover, since no paired data is provided, we propose a novel cross-domain training scheme using data from two domains with the designed analogy constraint.
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models
The detection of malicious deepfakes is a constantly evolving problem that requires continuous monitoring of detectors to ensure they can detect image manipulations generated by the latest emerging models.
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations
Such representations along with 3D tracking can be used as self-supervision to train a generator with control over coarse expressions and finer facial attributes.
