no code implementations • ICCV 2023 • Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Kunhao Liu, Rongliang Wu, Xiaoqin Zhang, Ling Shao, Shijian Lu
However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization.
no code implementations • 18 Apr 2023 • Rongliang Wu, Yingchen Yu, Fangneng Zhan, Jiahui Zhang, Xiaoqin Zhang, Shijian Lu
To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence.
no code implementations • 18 Apr 2023 • Rongliang Wu, Yingchen Yu, Fangneng Zhan, Jiahui Zhang, Shengcai Liao, Shijian Lu
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
no code implementations • 5 Apr 2023 • Kaiwen Cui, Rongliang Wu, Fangneng Zhan, Shijian Lu
Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces.
no code implementations • 4 Aug 2022 • Jiahui Zhang, Fangneng Zhan, Yingchen Yu, Rongliang Wu, Xiaoqin Zhang, Shijian Lu
In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.
no code implementations • 21 Jul 2022 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Changgong Zhang, Shijian Lu
Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
1 code implementation • 6 Jul 2022 • Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jiahui Zhang, Shijian Lu, Miaomiao Cui, Xuansong Xie, Xian-Sheng Hua, Chunyan Miao
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
no code implementations • 6 Jul 2022 • Jiahui Zhang, Fangneng Zhan, Rongliang Wu, Yingchen Yu, Wenqing Zhang, Bai Song, Xiaoqin Zhang, Shijian Lu
With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.
no code implementations • CVPR 2022 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang
We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.
1 code implementation • CVPR 2022 • Fangneng Zhan, Jiahui Zhang, Yingchen Yu, Rongliang Wu, Shijian Lu
Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.
2 code implementations • 27 Dec 2021 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric Xing
With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.
2 code implementations • 7 Jul 2021 • Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Kaiwen Cui, Aoran Xiao, Shijian Lu, Chunyan Miao
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
no code implementations • 26 Apr 2021 • Yingchen Yu, Fangneng Zhan, Rongliang Wu, Jianxiong Pan, Kaiwen Cui, Shijian Lu, Feiying Ma, Xuansong Xie, Chunyan Miao
With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions.
1 code implementation • 20 Feb 2021 • Fangneng Zhan, Yingchen Yu, Changgong Zhang, Rongliang Wu, WenBo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao
This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.
no code implementations • ECCV 2020 • Rongliang Wu, Shijian Lu
Recent studies on facial expression editing have obtained very promising progress.
no code implementations • CVPR 2020 • Rongliang Wu, Gongjie Zhang, Shijian Lu, Tao Chen
Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing.