13 citations · 27 across the 7 of their papers we have counts for
7 papers
BadWorld: Adversarial Attacks on World Models
Linghui Shen, Mingyue Cui, Xingyi Yang
Visual world models (VWMs) synthesize interactive, action-conditioned rollouts from a single context image. However, it remains an open question how robust these models are to adve…
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Fa-Ting Hong, Li Shen, Dan Xu
Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial g…
DGL-GAN: Discriminator Guided Learning for GAN Compression
Yuesong Tian, Li Shen, Xiang Tian +4
Generative Adversarial Networks (GANs) with high computation costs, e.g., BigGAN and StyleGAN2, have achieved remarkable results in synthesizing high-resolution images from random…
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Meng Cao, Haozhi Huang, Hao Wang +6
Recent research has witnessed advances in facial image editing tasks including face swapping and face reenactment. However, these methods are confined to dealing with one specific…
Attacking Adversarial Attacks as A Defense
Boxi Wu, Heng Pan, Li Shen +6
It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations. Although adversarial training significantly improves model robustness, fai…
Task-agnostic Temporally Consistent Facial Video Editing
Meng Cao, Haozhi Huang, Hao Wang +6
Recent research has witnessed the advances in facial image editing tasks. For video editing, however, previous methods either simply apply transformations frame by frame or utilize…
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks
Yuesong Tian, Li Shen, Guinan Su +2
Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning aga…