6 citations · 8 across the 11 of their papers we have counts for
7 papers · 1 filter
The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview
Zheng Chen, Kai Liu, Jingkai Wang +150
This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…
Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention
Jeonghoon Park, Juyoung Lee, Chaeyeon Chung +3
Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text. However, they often exhibit socie…
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
Junha Hyung, Susung Hong, Sungwon Hwang +3
3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising tec…
SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder
Jaeseong Lee, Junha Hyung, Sohyun Jeong +1
Face swapping has gained significant attention for its varied applications. Most previous face swapping approaches have relied on the seesaw game training scheme, also known as the…
Expression Domain Translation Network for Cross-domain Head Reenactment
Taewoong Kang, Jeongsik Oh, Jaeseong Lee +2
Despite the remarkable advancements in head reenactment, the existing methods face challenges in cross-domain head reenactment, which aims to transfer human motions to domains outs…
PixelHuman: Animatable Neural Radiance Fields from Few Images
Gyumin Shim, Jaeseong Lee, Junha Hyung +1
In this paper, we propose PixelHuman, a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses. Pre…
RobustSwap: A Simple yet Robust Face Swapping Model against Attribute Leakage
Jaeseong Lee, Taewoo Kim, Sunghyun Park +2
Face swapping aims at injecting a source image's identity (i.e., facial features) into a target image, while strictly preserving the target's attributes, which are irrelevant to id…