1 citations · 1 across the 11 of their papers we have counts for
12 papers
PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation
Chunghyun Park, Beomjun Kim, Seungcheol Park +5
World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches ty…
Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards
Seungwook Kim, Minsu Cho
Text-to-image generation powers content creation across design, media, and data augmentation. Post-training of text-to-image generative models is a promising path to improve human…
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…
Combinative Matching for Geometric Shape Assembly
Nahyuk Lee, Juhong Min, Junhong Lee +2
This paper introduces a new shape-matching methodology, combinative matching, to combine interlocking parts for geometric shape assembly. Previous methods for geometric assembly ty…
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…
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation
Junha Lee, Chunghyun Park, Jaesung Choe +4
We tackle open-vocabulary 3D scene understanding by introducing a novel data generation pipeline and training framework. Our method addresses three critical requirements for effect…
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…