activity
20222024
most citedA Dataset of Relighted 3D Interacting Hands

4 citations · 8 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV20241 cited

Expressive Whole-Body 3D Gaussian Avatar

Gyeongsik Moon, Takaaki Shiratori, Shunsuke Saito

Facial expression and hand motions are necessary to express our emotions and interact with the world. Nevertheless, most of the 3D human avatars modeled from a casually captured vi…

cs.CV2024

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…

cs.CV2024

URHand: Universal Relightable Hands

Zhaoxi Chen, Gyeongsik Moon, Kaiwen Guo +20

Existing photorealistic relightable hand models require extensive identity-specific observations in different views, poses, and illuminations, and face challenges in generalizing t…

cs.CV20234 cited

A Dataset of Relighted 3D Interacting Hands

Gyeongsik Moon, Shunsuke Saito, Weipeng Xu +12

The two-hand interaction is one of the most challenging signals to analyze due to the self-similarity, complicated articulations, and occlusions of hands. Although several datasets…

cs.CV20231 cited

Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

JoonKyu Park, Daniel Sungho Jung, Gyeongsik Moon +1

Understanding how two hands interact with each other is a key component of accurate 3D interacting hand mesh recovery. However, recent Transformer-based methods struggle to learn t…

cs.CV2023

Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild

Gyeongsik Moon, Hongsuk Choi, Sanghyuk Chun +2

Recovering 3D human mesh in the wild is greatly challenging as in-the-wild (ITW) datasets provide only 2D pose ground truths (GTs). Recently, 3D pseudo-GTs have been widely used to…