1 code implementation • CVPR 2023 • Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Houqiang Li
In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection.
1 code implementation • ICCV 2023 • Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li
We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.
no code implementations • 28 Nov 2022 • Hezhen Hu, Weilun Wang, Wengang Zhou, Houqiang Li
In this work, we are dedicated to a new task, i. e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status.
no code implementations • 28 Nov 2022 • YiXuan Wang, Wengang Zhou, Jianmin Bao, Weilun Wang, Li Li, Houqiang Li
The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network.
1 code implementation • 22 Nov 2022 • Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, Houqiang Li
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
Ranked #1 on Image Generation on Places50
3 code implementations • 30 Jun 2022 • Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, Houqiang Li
Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs).
no code implementations • 25 Aug 2021 • Xiao Cui, Wengang Zhou, Yang Hu, Weilun Wang, Houqiang Li
The main idea is to disentangle the latent space of a pre-trained generation model and precisely control the face attributes of child images with clear semantics.
no code implementations • ICCV 2021 • Weilun Wang, Wengang Zhou, Jianmin Bao, Dong Chen, Houqiang Li
In this paper, we uncover that the negative examples play a critical role in the performance of contrastive learning for image translation.
no code implementations • CVPR 2021 • Hezhen Hu, Weilun Wang, Wengang Zhou, Weichao Zhao, Houqiang Li
Then, a transformation flow is calculated based on the correspondence of the source and target topology map.