Search Results for author:
Found 15 papers, 4 papers with code
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images
By contrast, it is hard to collect paired data for in-the-wild scenes, and therefore, virtual try-on for casual images of people against cluttered backgrounds is rarely studied.
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ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
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.
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
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.
Make It So: Steering StyleGAN for Any Image Inversion and Editing
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.
MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN
Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image.
Near Perfect GAN Inversion
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.
CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define.
TweeNLP: A Twitter Exploration Portal for Natural Language Processing
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.
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
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).
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
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.
Signal Reconstruction from Modulo Observations
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements).
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
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.
Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees
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.
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
We consider the problem of reconstructing signals and images from periodic nonlinearities.
