7 papers
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…
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…
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…