activity
20202024
most citedImage Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

42 citations · 43 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Reference-based Controllable Scene Stylization with Gaussian Splatting

Yiqun Mei, Jiacong Xu, Vishal M. Patel

Referenced-based scene stylization that edits the appearance based on a content-aligned reference image is an emerging research area. Starting with a pretrained neural radiance fie…

cs.CV20241 cited

Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections

Jiacong Xu, Yiqun Mei, Vishal M. Patel

Photographs captured in unstructured tourist environments frequently exhibit variable appearances and transient occlusions, challenging accurate scene reconstruction and inducing a…

cs.CV2024

Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

Yiqun Mei, Yu Zeng, He Zhang +6

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In t…

cs.CV2023

LightPainter: Interactive Portrait Relighting with Freehand Scribble

Yiqun Mei, He Zhang, Xuaner Zhang +7

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these m…

cs.CV2022

Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination

Yiqun Mei, Pengfei Guo, Vishal M. Patel

In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult pr…

cs.CV202042 cited

Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining

Yiqun Mei, Yuchen Fan, Yuqian Zhou +3

Deep convolution-based single image super-resolution (SISR) networks embrace the benefits of learning from large-scale external image resources for local recovery, yet most existin…