114 citations · 195 across the 22 of their papers we have counts for
10 papers · 1 filter
Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration
Sangwoo Jo, Donggeun Ko, Jayeon Kang +3
Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for p…
Learning Joint Representation of Human Motion and Language
Jihoon Kim, Youngjae Yu, Seungyoun Shin +2
In this work, we present MoLang (a Motion-Language connecting model) for learning joint representation of human motion and language, leveraging both unpaired and paired datasets of…
FLAME: Free-form Language-based Motion Synthesis & Editing
Jihoon Kim, Jiseob Kim, Sungjoon Choi
Text-based motion generation models are drawing a surge of interest for their potential for automating the motion-making process in the game, animation, or robot industries. In thi…
Conditional Motion In-betweening
Jihoon Kim, Taehyun Byun, Seungyoun Shin +2
Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such a…
Polyblur: Removing mild blur by polynomial reblurring
Mauricio Delbracio, Ignacio Garcia-Dorado, Sungjoon Choi +2
We present a highly efficient blind restoration method to remove mild blur in natural images. Contrary to the mainstream, we focus on removing slight blur that is often present, da…
Fast, Trainable, Multiscale Denoising
Sungjoon Choi, John Isidoro, Pascal Getreuer +1
Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows re…
BLADE: Filter Learning for General Purpose Computational Photography
Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro +3
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters.…
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modeling
Sungjoon Choi, Kyungjae Lee, Sungbin Lim +1
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriat…
Unsupervised Holistic Image Generation from Key Local Patches
Donghoon Lee, Sangdoo Yun, Sungjoon Choi +3
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informati…
A Large Dataset of Object Scans
Sungjoon Choi, Qian-Yi Zhou, Stephen Miller +1
We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning…