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Inès Hyeonsu Kim

4 accepted papers

2026

AnthroTAP: Learning Point Tracking with Real-World Motion

CVPR 2026

Point tracking models often struggle to generalize to real-world videos because large-scale training data is predominantly synthetic--the only source currently feasible to produce at scale. Collecting real-world annotations, however, is prohibitively expensive, as it requires tracking hundreds of po

Cited by 0SourcecodeScholar
2026

MV-TAP: Tracking Any Point in Multi-View Videos

CVPR 2026

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. Point tracking serves as a key mechanism for capturing dynamic motion. However, conventional single-view approaches of

Cited by 0SourcecodeScholar
2025

Exploring Temporally-Aware Features for Point Tracking

CVPR 2025poster

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 generalizability, point tracking has primarily relied on simpler backbones trained from scratch on synthetic data, whic…

2024

Retrieval-Augmented Score Distillation for Text-to-3D Generation

ICML 2024poster

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 geometry. Recently, since large-scale multi-view datasets have been released, fine-tuning the diffusion model on the multi-v…