Search Results for author:
Found 11 papers, 6 papers with code
CATSplat: Context-Aware Transformer with Spatial Guidance for Generalizable 3D Gaussian Splatting from A Single-View Image
Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources.
Fashion-VDM: Video Diffusion Model for Virtual Try-On
We present Fashion-VDM, a video diffusion model (VDM) for generating virtual try-on videos.
Ranked #1 on
Virtual Try-on
on UBC Fashion Videos
ReBotNet: Fast Real-time Video Enhancement
The first branch learns spatio-temporal features by tokenizing the input frames along the spatial and temporal dimensions using a ConvNext-based encoder and processing these abstract tokens using a bottleneck mixer.
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution
Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions.
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.
Ranked #4 on
Image Rescaling
on DIV2K val-q30-4x
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
We propose DeFlow, a method for learning stochastic image degradations from unpaired data.
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
Ranked #8 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
For training, only one set of source input images is therefore provided in the challenge.
Unsupervised Learning for Real-World Super-Resolution
Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image.
