no code implementations • 21 Apr 2024 • Zhanjie Zhang, Jiakai Sun, Guangyuan Li, Lei Zhao, Quanwei Zhang, Zehua Lan, Haolin Yin, Wei Xing, Huaizhong Lin, Zhiwen Zuo
Arbitrary style transfer holds widespread attention in research and boasts numerous practical applications.
1 code implementation • 23 Mar 2023 • Zhiwen Zuo, Lei Zhao, Ailin Li, Zhizhong Wang, Zhanjie Zhang, Jiafu Chen, Wei Xing, Dongming Lu
By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes.
no code implementations • ICCV 2023 • Tianyi Chu, Jiafu Chen, Jiakai Sun, Shuobin Lian, Zhizhong Wang, Zhiwen Zuo, Lei Zhao, Wei Xing, Dongming Lu
Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification.
1 code implementation • 28 Nov 2022 • Zhizhong Wang, Lei Zhao, Zhiwen Zuo, Ailin Li, Haibo Chen, Wei Xing, Dongming Lu
The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations.
1 code implementation • 27 Aug 2022 • Zhizhong Wang, Zhanjie Zhang, Lei Zhao, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings.
1 code implementation • 6 Dec 2021 • Zhizhong Wang, Lei Zhao, Haibo Chen, Ailin Li, Zhiwen Zuo, Wei Xing, Dongming Lu
In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer.
1 code implementation • NeurIPS 2021 • Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns.
no code implementations • CVPR 2021 • Haibo Chen, Lei Zhao, Zhizhong Wang, Huiming Zhang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles.
no code implementations • 16 Jan 2021 • Zhizhong Wang, Lei Zhao, Haibo Chen, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Gram-based and patch-based approaches are two important research lines of style transfer.
no code implementations • ICCV 2021 • Haibo Chen, Lei Zhao, Huiming Zhang, Zhizhong Wang, Zhiwen Zuo, Ailin Li, Wei Xing, Dongming Lu
Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images.
no code implementations • 8 Aug 2020 • Zhiwen Zuo, Lei Zhao, Zhizhong Wang, Haibo Chen, Ailin Li, Qijiang Xu, Wei Xing, Dongming Lu
Multimodal image-to-image translation (I2IT) aims to learn a conditional distribution that explores multiple possible images in the target domain given an input image in the source domain.
no code implementations • ICLR 2020 • Zhiwen Zuo, Lei Zhao, Huiming Zhang, Qihang Mo, Haibo Chen, Zhizhong Wang, Ailin Li, Lihong Qiu, Wei Xing, Dongming Lu
Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images.
no code implementations • ICLR 2020 • Zhizhong Wang, Lei Zhao, Qihang Mo, Sihuan Lin, Zhiwen Zuo, Wei Xing, Dongming Lu
This could help improve the quality and flexibility, and guide us to find domain-independent approaches.
no code implementations • 21 Jan 2019 • Zhiwen Zuo, Lei Zhao, Liwen Zuo, Feng Jiang, Wei Xing, Dongming Lu
Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep Belif Networks(DBNs), are powerful in automatic feature extraction, unsupervised weight initialization and density estimation.