Face Reenactment

8 papers with code • 0 benchmarks • 0 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

Datasets


Greatest papers with code

One-shot Face Reenactment

bj80heyue/Learning_One_Shot_Face_Reenactment 5 Aug 2019

However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable.

Face Reconstruction Face Reenactment

ReenactGAN: Learning to Reenact Faces via Boundary Transfer

wywu/ReenactGAN ECCV 2018

A transformer is subsequently used to adapt the boundary of source face to the boundary of target face.

Face Reenactment Talking Face Generation +1

ICface: Interpretable and Controllable Face Reenactment Using GANs

Blade6570/icface 3 Apr 2019

This paper presents a generic face animator that is able to control the pose and expressions of a given face image.

Face Reenactment

APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment

zhangzjn/APB2FaceV2 25 Oct 2020

Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.

Face Reenactment

APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals

zhangzjn/APB2FaceV2 30 Apr 2020

Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person.

Face Reenactment

Everything's Talkin': Pareidolia Face Reenactment

Linsen13/EverythingTalking 7 Apr 2021

We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video.

Face Reenactment Texture Synthesis

SMILE: Semantically-guided Multi-attribute Image and Layout Editing

affromero/SMILE 5 Oct 2020

Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.

Face Reenactment Image Manipulation