Sunghwan Hong

LIME turns ordinary egocentric video into a powerful tool for robots to dynamically adjust their camera poses based on user intent, revolutionizing how we think about robotic perception.

Achieving state-of-the-art registration accuracy without any learned parameters or depth sensors, PROSE redefines the landscape of egocentric scene understanding.

GAM revolutionizes robot policy learning by seamlessly integrating 3D geometric reasoning, outperforming traditional models in accuracy and efficiency.

Achieving six times fewer Gaussians while surpassing state-of-the-art performance redefines efficiency in 3D scene reconstruction.

Forget training wheels: this training-free method leverages uncertainty to guide vision-language models to the right image regions, boosting performance on detail-oriented tasks.

Forget sparse annotations: a new dataset and compact VLM show that dense, complete screen parsing supervision unlocks substantial gains in UI understanding and grounding, even for large foundation models.