1 code implementation • 23 May 2023 • Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su
We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation.
no code implementations • CVPR 2023 • Rameen Abdal, Hsin-Ying Lee, Peihao Zhu, Menglei Chai, Aliaksandr Siarohin, Peter Wonka, Sergey Tulyakov
Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains.
no code implementations • 27 May 2022 • Rameen Abdal, Peihao Zhu, Niloy J. Mitra, Peter Wonka
Image editing using a pretrained StyleGAN generator has emerged as a powerful paradigm for facial editing, providing disentangled controls over age, expression, illumination, etc.
no code implementations • 9 Dec 2021 • Rameen Abdal, Peihao Zhu, John Femiani, Niloy J. Mitra, Peter Wonka
The success of StyleGAN has enabled unprecedented semantic editing capabilities, on both synthesized and real images.
2 code implementations • ICLR 2022 • Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka
The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B.
no code implementations • ICCV 2021 • Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi
We consider the problem of filling in missing spatio-temporal regions of a video.
1 code implementation • 2 Jun 2021 • Peihao Zhu, Rameen Abdal, John Femiani, Peter Wonka
Seamlessly blending features from multiple images is extremely challenging because of complex relationships in lighting, geometry, and partial occlusion which cause coupling between different parts of the image.
1 code implementation • ICCV 2021 • Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka
We propose an unsupervised segmentation framework for StyleGAN generated objects.
3 code implementations • 13 Dec 2020 • Peihao Zhu, Rameen Abdal, Yipeng Qin, John Femiani, Peter Wonka
First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes.
no code implementations • 25 Aug 2020 • Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi
We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error.
3 code implementations • 6 Aug 2020 • Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka
We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images.
1 code implementation • CVPR 2020 • Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka
Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e. g., we can specify one style reference image per region.