1 code implementation • 27 Nov 2023 • Aiyu Cui, Jay Mahajan, Viraj Shah, Preeti Gomathinayagam, Chang Liu, Svetlana Lazebnik
By contrast, it is hard to collect paired data for in-the-wild scenes, and therefore, virtual try-on for casual images of people with more diverse poses against cluttered backgrounds is rarely studied.
Ranked #1 on Virtual Try-on on StreetTryOn
1 code implementation • 22 Nov 2023 • Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana Lazebnik, Yuanzhen Li, Varun Jampani
Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize.
no code implementations • 18 May 2023 • Viraj Shah, Svetlana Lazebnik, Julien Philip
In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materials.
no code implementations • 27 Apr 2023 • Anand Bhattad, Viraj Shah, Derek Hoiem, D. A. Forsyth
StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge.
no code implementations • 8 Oct 2022 • Viraj Shah, Ayush Sarkar, Sudharsan Krishnakumar Anitha, Svetlana Lazebnik
Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image.
no code implementations • 23 Feb 2022 • Qianli Feng, Viraj Shah, Raghudeep Gadde, Pietro Perona, Aleix Martinez
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it.
no code implementations • 26 Oct 2021 • Kalina Borkiewicz, Viraj Shah, J. P. Naiman, Chuanyue Shen, Stuart Levy, Jeff Carpenter
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define.
no code implementations • ACL 2021 • Viraj Shah, Shruti Singh, Mayank Singh
It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers.
no code implementations • 13 May 2021 • Viraj Shah, Rakib Hyder, M. Salman Asif, Chinmay Hegde
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by deep generative networks).
no code implementations • 4 Jun 2019 • Viraj Shah, Ameya Joshi, Sambuddha Ghosal, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
no code implementations • 7 Mar 2019 • Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif
We empirically show that the performance of our method with projected gradient descent is superior to the existing approach for solving phase retrieval under generative priors.
1 code implementation • 3 Dec 2018 • Viraj Shah, Chinmay Hegde
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements).
no code implementations • 21 Nov 2018 • Rahul Singh, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde
The first model is a WGAN model that uses a finite number of training images to synthesize new microstructures that weakly satisfy the physical invariances respected by the original data.
1 code implementation • 23 Feb 2018 • Viraj Shah, Chinmay Hegde
In this work, we advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing.
no code implementations • 29 Sep 2017 • Viraj Shah, Mohammadreza Soltani, Chinmay Hegde
We consider the problem of reconstructing signals and images from periodic nonlinearities.