no code implementations • 16 Oct 2023 • Long Zhuo, Shenghai Luo, Shunquan Tan, Han Chen, Bin Li, Jiwu Huang
In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces.
1 code implementation • NeurIPS 2023 • Dongwei Pan, Long Zhuo, Jingtan Piao, Huiwen Luo, Wei Cheng, Yuxin Wang, Siming Fan, Shengqi Liu, Lei Yang, Bo Dai, Ziwei Liu, Chen Change Loy, Chen Qian, Wayne Wu, Dahua Lin, Kwan-Yee Lin
It is a large-scale digital library for head avatars with three key attributes: 1) High Fidelity: all subjects are captured by 60 synchronized, high-resolution 2K cameras in 360 degrees.
1 code implementation • 11 Jul 2022 • Long Zhuo, Guangcong Wang, Shikai Li, Wayne Wu, Ziwei Liu
In this paper, we present a spatial-temporal compression framework, \textbf{Fast-Vid2Vid}, which focuses on data aspects of generative models.
1 code implementation • 6 Jul 2021 • Long Zhuo, Shunquan Tan, Bin Li, Jiwu Huang
In this paper, we propose a self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a self-attention mechanism to localize forged regions in forgery images.