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20172022
most citedDFGC 2021: A DeepFake Game Competition

19 citations · 53 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CV20221 cited

Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation

Yunfan Liu, Qi Li, Zhenan Sun +1

One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of…

cs.CV20221 cited

GAN-based Facial Attribute Manipulation

Yunfan Liu, Qi Li, Qiyao Deng +2

Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practica…

cs.CV20225 cited

AnyFace: Free-style Text-to-Face Synthesis and Manipulation

Jianxin Sun, Qiyao Deng, Qi Li +3

Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So…

cs.CV202119 cited

DFGC 2021: A DeepFake Game Competition

Bo Peng, Hongxing Fan, Wei Wang +20

This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the s…

cs.CV202016 cited

Style Intervention: How to Achieve Spatial Disentanglement with Style-based Generators?

Yunfan Liu, Qi Li, Zhenan Sun +1

Generative Adversarial Networks (GANs) with style-based generators (e.g. StyleGAN) successfully enable semantic control over image synthesis, and recent studies have also revealed…

cs.CV20205 cited

Reference-guided Face Component Editing

Qiyao Deng, Jie Cao, Yunfan Liu +3

Face portrait editing has achieved great progress in recent years. However, previous methods either 1) operate on pre-defined face attributes, lacking the flexibility of controllin…