2 citations · 2 across the 4 of their papers we have counts for
7 papers
PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation
Sangmin Hong, Daniel Sungho Jung, Heewon Kim +1
Point clouds are one of the most fundamental and widely used 3D representations, serving as the most basic geometric representation of 3D shapes. Nevertheless, most existing 3D pri…
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 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…
Grokfast: Accelerated Grokking by Amplifying Slow Gradients
Jaerin Lee, Bong Gyun Kang, Kihoon Kim +1
One puzzling artifact in machine learning dubbed grokking is where delayed generalization is achieved tenfolds of iterations after near perfect overfitting to the training data. Fo…
Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer
Hyeongjin Nam, Daniel Sungho Jung, Gyeongsik Moon +1
Human-object contact serves as a strong cue to understand how humans physically interact with objects. Nevertheless, it is not widely explored to utilize human-object contact infor…
SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models
Jaerin Lee, Daniel Sungho Jung, Kanggeon Lee +1
We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each enc…