6 papers
Probing and Leveraging Video Diffusion Transformer Features for Robust Point Tracking
Soowon Son, Honggyu An, Jisu Nam +7
Despite achieving strong results on standard benchmarks, current point tracking methods rely on feature backbones that are rarely designed with the temporal coherence needed for ro…
C3G: Learning Compact 3D Representations with 2K Gaussians
Honggyu An, Jaewoo Jung, Mungyeom Kim +10
Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3…
DUSt3R: Enhancing 3D Reconstruction for Dynamic Scenes
Jisang Han, Honggyu An, Jaewoo Jung +7
In this work, we address the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods, such as DU…
Visual Representation Alignment for Multimodal Large Language Models
Heeji Yoon, Jaewoo Jung, Junwan Kim +10
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tas…
Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers
Chaehyun Kim, Heeseong Shin, Eunbeen Hong +5
Text-to-image diffusion models excel at translating language prompts into photorealistic images by implicitly grounding textual concepts through their cross-modal attention mechani…
Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels
Heeseong Shin, Chaehyun Kim, Sunghwan Hong +4
Large-scale vision-language models like CLIP have demonstrated impressive open-vocabulary capabilities for image-level tasks, excelling in recognizing what objects are present. How…