10 citations · 15 across the 16 of their papers we have counts for
17 papers
Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance
Myeongseok Nam, Donghun Yeo, Seungwook Kim
Synthetic images are increasingly used to augment scarce real data for object detection. However, not all synthetic sets help equally, and the only way to know a set's value is to…
RapidMV: Leveraging Spatio-Angular Representations for Efficient and Consistent Text-to-Multi-View Synthesis
Seungwook Kim, Yichun Shi, Kejie Li +2
Generating synthetic multi-view images from a text prompt is an essential bridge to generating synthetic 3D assets. In this work, we introduce RapidMV, a novel text-to-multi-view g…
Similarity-Aware Selective State-Space Modeling for Semantic Correspondence
Seungwook Kim, Minsu Cho
Establishing semantic correspondences between images is a fundamental yet challenging task in computer vision. Traditional feature-metric methods enhance visual features but may mi…
FreeAction: Training-Free Techniques for Enhanced Fidelity of Trajectory-to-Video Generation
Seungwook Kim, Seunghyeon Lee, Minsu Cho
Generating realistic robot videos from explicit action trajectories is a critical step toward building effective world models and robotics foundation models. We introduce two train…
Harnessing the Power of Training-Free Techniques in Text-to-2D Generation for Text-to-3D Generation via Score Distillation Sampling
Junhong Lee, Seungwook Kim, Minsu Cho
Recent studies show that simple training-free techniques can dramatically improve the quality of text-to-2D generation outputs, e.g. Classifier-Free Guidance (CFG) or FreeU. Howeve…
3D Geometric Shape Assembly via Efficient Point Cloud Matching
Nahyuk Lee, Juhong Min, Junha Lee +4
Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local…
Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation
Seungwook Kim, Yichun Shi, Kejie Li +2
Using image as prompts for 3D generation demonstrate particularly strong performances compared to using text prompts alone, for images provide a more intuitive guidance for the 3D…
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
Chunghyun Park, Seungwook Kim, Jaesik Park +1
Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics. However, existing self-supervised…
CorrespondentDream: Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Seungwook Kim, Kejie Li, Xueqing Deng +3
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e.g., the Janus face problem or the content drift problem, in ze…
Efficient Semantic Matching with Hypercolumn Correlation
Seungwook Kim, Juhong Min, Minsu Cho
Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the…
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…
Learning Rotation-Equivariant Features for Visual Correspondence
Jongmin Lee, Byungjin Kim, Seungwook Kim +1
Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a s…
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning
Hyounguk Shon, Janghyeon Lee, Seung Hwan Kim +1
Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning…
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
Seungwook Kim, Juhong Min, Minsu Cho
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this wor…
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
Juhong Min, Seungwook Kim, Minsu Cho
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this wor…
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…
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Hyo-Eun Kim, Seungwook Kim, Jaehwan Lee
Data is one of the most important factors in machine learning. However, even if we have high-quality data, there is a situation in which access to the data is restricted. For examp…