From the 1 of 7 linked papers with an AI index.
7 papers
PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation
Sangmin Hong, Daniel Sungho Jung, Heewon Kim +1
PrintAnything is a framework that learns to generate executable 3D‑printing G‑code directly from raw point clouds by projecting them slice‑wise into a compact 2D G‑plan representat…
Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models
Daniel Sungho Jung, Kyoung Mu Lee
Dense hand contact estimation requires both high-level semantic understanding and fine-grained geometric reasoning of human interaction to accurately localize contact regions. Rece…
Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation
Daniel Sungho Jung, Kyoung Mu Lee
Foot contact plays a critical role in human interaction with the world, and thus exploring foot contact can advance our understanding of human movement and physical interaction. De…
Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds
Daniel Sungho Jung, Dohee Cho, Kyoung Mu Lee
Understanding humans from LiDAR point clouds is one of the most critical tasks in autonomous driving due to its close relationships with pedestrian safety, yet it remains challengi…
TeHOR: Text-Guided 3D Human and Object Reconstruction with Textures
Hyeongjin Nam, Daniel Sungho Jung, Kyoung Mu Lee
Joint reconstruction of 3D human and object from a single image is an active research area, with pivotal applications in robotics and digital content creation. Despite recent advan…
Learning Dense Hand Contact Estimation from Imbalanced Data
Daniel Sungho Jung, Kyoung Mu Lee
Hands are essential to human interaction, and exploring contact between hands and the world can promote comprehensive understanding of their function. Recently, there have been gro…