18 citations · 74 across the 21 of their papers we have counts for
25 papers
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Seungwook Kim, Chunghyun Park, Yoonwoo Jeong +2
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely va…
Fill-Up: Balancing Long-Tailed Data with Generative Models
Joonghyuk Shin, Minguk Kang, Jaesik Park
Modern text-to-image synthesis models have achieved an exceptional level of photorealism, generating high-quality images from arbitrary text descriptions. In light of the impressiv…
Extending CLIP's Image-Text Alignment to Referring Image Segmentation
Seoyeon Kim, Minguk Kang, Dongwon Kim +2
Referring Image Segmentation (RIS) is a cross-modal task that aims to segment an instance described by a natural language expression. Recent methods leverage large-scale pretrained…
Binary Radiance Fields
Seungjoo Shin, Jaesik Park
In this paper, we propose \textit{binary radiance fields} (BiRF), a storage-efficient radiance field representation employing binary feature encoding that encodes local features us…
Scaling up GANs for Text-to-Image Synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang +4
The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic chan…
Substructure-Atom Cross Attention for Molecular Representation Learning
Jiye Kim, Seungbeom Lee, Dongwoo Kim +2
Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design. In this work, we propose a new framework for molec…
Instance-Aware Image Completion
Jinoh Cho, Minguk Kang, Vibhav Vineet +1
Image completion is a task that aims to fill in the missing region of a masked image with plausible contents. However, existing image completion methods tend to fill in the missing…
Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction
Seokjun Ahn, Jungtaek Kim, Minsu Cho +1
We tackle the problem of sequential brick assembly with LEGO bricks to create combinatorial 3D structures. This problem is challenging since this brick assembly task encompasses th…
Style-Agnostic Reinforcement Learning
Juyong Lee, Seokjun Ahn, Jaesik Park
We present a novel method of learning style-agnostic representation using both style transfer and adversarial learning in the reinforcement learning framework. The style, here, ref…
Learning to Assemble Geometric Shapes
Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung +2
Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Pre…
Learning Debiased Classifier with Biased Committee
Nayeong Kim, Sehyun Hwang, Sungsoo Ahn +2
Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generali…
StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis
Minguk Kang, Joonghyuk Shin, Jaesik Park
Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating GAN becomes increasingly importan…
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning
Seunghyuk Cho, Juyong Lee, Jaesik Park +1
We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain…
Fast Point Transformer
Chunghyun Park, Yoonwoo Jeong, Minsu Cho +1
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approach…
Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective
Wei Dong, Kwonyoung Ryu, Michael Kaess +1
Spinning LiDAR data are prevalent for 3D vision tasks. Since LiDAR data is presented in the form of point clouds, expensive 3D operations are usually required. This paper revisits…
Putting 3D Spatially Sparse Networks on a Diet
Junha Lee, Christopher Choy, Jaesik Park
3D neural networks have become prevalent for many 3D vision tasks including object detection, segmentation, registration, and various perception tasks for 3D inputs. However, due t…
Instance-wise Occlusion and Depth Orders in Natural Scenes
Hyunmin Lee, Jaesik Park
In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the geometrical relationships of instances in an image. The dataset consists of 2.9M ann…
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training
Minguk Kang, Woohyeon Shim, Minsu Cho +1
Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classi…
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning
Hyunsoo Chung, Jungtaek Kim, Boris Knyazev +4
Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possib…
HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge
Jae Shin Yoon, Zhixuan Yu, Jaesik Park +1
This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearanc…
Deep Hough Voting for Robust Global Registration
Junha Lee, Seungwook Kim, Minsu Cho +1
Point cloud registration is the task of estimating the rigid transformation that aligns a pair of point cloud fragments. We present an efficient and robust framework for pairwise r…
Self-Calibrating Neural Radiance Fields
Yoonwoo Jeong, Seokjun Ahn, Christopher Choy +3
In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate…
Realistic Image Synthesis with Configurable 3D Scene Layouts
Jaebong Jeong, Janghun Jo, Jingdong Wang +2
Recent conditional image synthesis approaches provide high-quality synthesized images. However, it is still challenging to accurately adjust image contents such as the positions an…
Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB-D Camera
Hyomin Kim, Jungeon Kim, Hyeonseo Nam +2
This paper presents an effective method for generating a spatiotemporal (time-varying) texture map for a dynamic object using a single RGB-D camera. The input of our framework is a…