5 papers
AnthroTAP: Learning Point Tracking with Real-World Motion
Inès Hyeonsu Kim, Seokju Cho, Jahyeok Koo +5
Point tracking models often struggle to generalize to real-world videos because large-scale training data is predominantly synthetic$\unicode{x2014}$the only source currently feasi…
MV-TAP: Tracking Any Point in Multi-View Videos
Jahyeok Koo, Inès Hyeonsu Kim, Mungyeom Kim +6
Multi-view camera systems enable rich observations of complex real-world scenes, and understanding dynamic objects in multi-view settings has become central to various applications…
Exploring Temporally-Aware Features for Point Tracking
Inès Hyeonsu Kim, Seokju Cho, Jiahui Huang +3
Point tracking in videos is a fundamental task with applications in robotics, video editing, and more. While many vision tasks benefit from pre-trained feature backbones to improve…
Geometry-Aware Score Distillation via 3D Consistent Noising and Gradient Consistency Modeling
Min-Seop Kwak, Donghoon Ahn, Ines Hyeonsu Kim +2
Score distillation sampling (SDS), the methodology in which the score from pretrained 2D diffusion models is distilled into 3D representation, has recently brought significant adva…
Retrieval-Augmented Score Distillation for Text-to-3D Generation
Junyoung Seo, Susung Hong, Wooseok Jang +4
Text-to-3D generation has achieved significant success by incorporating powerful 2D diffusion models, but insufficient 3D prior knowledge also leads to the inconsistency of 3D geom…