Yifan Wang
Yifan Wang
Home
News
Talks
Publications
CV
Light
Dark
Automatic
super-resolution
“Neuralize” geometry processing pipeline
Fueled by the proliferation of consumer-level 3D acquisition devices and the growing accessibility of shape modeling applications for …
Mar 9, 2022 16:30 — 17:30
online
Video Super-Resolution Using An Artificial Neural Network
According to one implementation, a video processing system includes a computing platform having a hardware processor and a system …
Christopher Schroers
,
Wang Yifan
,
Federico Perazzi
,
Brian McWilliams
,
Alexander Sorkine-Hornung
Techniques for upscaling images generated with undetermined downscaling kernels
Christopher Schroers
,
Wang Yifan
, Victor Cornillère,
Olga Sorkine-Hornung
,
Abdelaziz Djelouah
Detail-Driven 3D Content Creation
A talk summarizing pretty much everything I’ve done in my IGL PhD.
Feb 3, 2021 15:00 — 16:00
online
Follow Toronto Geometry Colloquium
live recording
Pursuing high-resolution 3D Geometry with Deep Learning
Geometric details in 3D shapes is a defining factor in many industries such as AR/VR, VFX and design. However, creating high-fidelity …
Jun 11, 2020 21:00 — 21:30
online
Slides
Blind Image Super-Resolution with Spatially Variant Degradations
We propose a novel approach for single image super-resolution to simultaneously predict high resolution images and the degradation kernels.
Victor Cornillère,
Abdelaziz Djelouah
,
Wang Yifan
,
Olga Sorkine-Hornung
,
Christopher Schroers
PDF
Cite
Code
Supplemental
IGL Project Page
Patch-base progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based …
Wang Yifan
,
Shihao Wu
,
Hui Huang
,
Daniel Cohen-Or
,
Olga Sorkine-Hornung
PDF
Cite
Code
Dataset
Supplemental
IGL project page
A Fully Progressive Approach to Single-Image Super-Resolution
We set a new benchmark for single-image super-resolution by exploiting progressiveness both in architecture and training. The proposed multi-scale models,
ProSR
and
ProSRGan
, improve the reconstruction quality in terms of PSNR and visual quality respectively. ProSR is one of the winning teams.
Wang Yifan
,
Federico Perazzi
,
Brian McWilliams
,
Alexander Sorkine-Hornung
,
Olga Sorkine-Hornung
,
Christopher Schroers
PDF
Cite
Code
Dataset
Video
IGL project page
Single-Image Super-Resolution
Pushing the limit of “zoom-in"s.
Cite
×