1 code implementation • 9 Oct 2023 • Tom Kelly, John Femiani, Peter Wonka
We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images.
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.
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.
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 • 9 May 2018 • John Femiani, Wamiq Reyaz Para, Niloy Mitra, Peter Wonka
Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to 'see' intermediate output probabilities of the various facade element classes.